{"id":12035,"date":"2023-07-06T15:12:07","date_gmt":"2023-07-06T13:12:07","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=people&#038;p=12035"},"modified":"2025-04-17T10:18:50","modified_gmt":"2025-04-17T08:18:50","slug":"michal-grzeszczyk","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/michal-grzeszczyk\/","title":{"rendered":"Micha\u0142 Grzeszczyk"},"excerpt":{"rendered":"<p>PhD Student in Medical Imaging and Robotics<\/p>\n","protected":false},"featured_media":18594,"template":"","people_teams":[19,35],"class_list":["post-12035","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-medical-imaging-and-robotics-group"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Micha\u0142 Grzeszczyk - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/michal-grzeszczyk\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Micha\u0142 Grzeszczyk\" \/>\n<meta property=\"og:description\" content=\"PhD Student in Medical Imaging and Robotics\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/michal-grzeszczyk\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-17T08:18:50+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/Sano-Michal-Grzeszczyk.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"1000\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/michal-grzeszczyk\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/michal-grzeszczyk\\\/\",\"name\":\"Micha\u0142 Grzeszczyk - 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He is a graduate of Computer Science studies at the Warsaw University of Technology and Technical University of Berlin (dual-degree). At Sano he&#8217;ll be working on non-invasive pulmonary hypertension detection. He is passionate about utilizing of AI in various areas. After hours, he works with his friend on the mobile application <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.chefs_app.chefs\">Chefs&#8217;<\/a> which is devoted to storing and sharing cooking recipes coming from multiple sources like images or websites. In his free time he loves playing football and discovering new sport disciplines.<\/p>\n","email":"","social_media":[{"icon":{"ID":11994,"id":11994,"title":"linkedin","filename":"linkedin.svg","filesize":914,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/linkedin-2\/","alt":"","author":"5","description":"","caption":"","name":"linkedin-2","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:24:13","modified":"2023-07-06 11:24:13","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/www.linkedin.com\/in\/michal-grzeszczyk","name":"LinkedIn"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"PhD Student in","link_text":"Medical Imaging and Robotics","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/health-informatics-group-higs\/"},"publications":[{"ID":14853,"post_author":"5","post_date":"2024-01-10 20:52:21","post_date_gmt":"2024-01-10 19:52:21","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-wEgzdH\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>Plotka, Grzeszczyk<\/strong>, Brawura-Biskupski-Samaha, Gutaj, Lipa, Trzcinski, Isgum, Sanchez,<strong> Sitek<\/strong><\/h2>\n<!-- \/wp:heading -->","post_title":"BabyNet++: Fetal Birth Weight Prediction using Biometry Multimodal Data Acquired Less Than 24 Hours Before Delivery","post_excerpt":"In: Computers in Biology & Medicine, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"babynet-fetal-birth-weight-prediction-using-biometry-multimodal-data-acquired-less-than-24-hours-before-delivery","to_ping":"","pinged":"","post_modified":"2024-01-26 14:17:39","post_modified_gmt":"2024-01-26 13:17:39","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14853","menu_order":29,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15288,"post_author":"5","post_date":"2024-02-01 21:09:23","post_date_gmt":"2024-02-01 20:09:23","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-Hz4pcT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Michal K. Grzeszczyk, Paulina Adamczyk, Sylwia Marek, Ryszard Pr\u0119cikowski, Maciej Ku\u015b, M. Patrycja Lelujko, Rosmary Blanco, Tomasz Trzci\u0144ski, Arkadiusz Sitek, Maciej Malawski, Aneta Lisowska<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Uo3zsR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-L4VCME\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Uo3zsR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:group {\"layout\":{\"type\":\"constrained\"},\"epAnimationGeneratedClass\":\"edplus_anim-14y02Y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div class=\"wp-block-group eplus-wrapper\"><!-- wp:acf\/button {\"id\":\"block_65bbfa5f62c5d\",\"name\":\"acf\/button\",\"data\":{\"title\":\"\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/--><\/div>\n<!-- \/wp:group -->","post_title":"Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load","post_excerpt":"In: AMIA 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"can-gamification-reduce-the-burden-of-self-reporting-in-mhealth-applications-a-feasibility-study-using-machine-learning-from-smartwatch-data-to-estimate-cognitive-load","to_ping":"","pinged":"","post_modified":"2024-02-01 21:09:23","post_modified_gmt":"2024-02-01 20:09:23","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=15288","menu_order":19,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14988,"post_author":"5","post_date":"2024-01-18 09:30:11","post_date_gmt":"2024-01-18 08:30:11","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-YVdG8o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Michal K. Grzeszczyk, Anna Lisowska, Arkadiusz Sitek, Aneta Lisowska<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-YxqGE7\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-fJ6nbh\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions. While previous studies have captured physiological signals using wearable devices in laboratory settings, providing valuable insights into the relationship between physiological responses and mental states, the transfer of these findings to real-life scenarios is still in its nascent stages. Our research aims to bridge the gap between laboratory-based studies and real-life settings by leveraging consumer-grade wearables and self-report measures. We conducted a preliminary study involving 15 healthy participants to assess the efficacy of wearables in capturing user valence in real-world settings. In this paper, we present the initial analysis of the collected data, focusing primarily on the results of valence classification. Our findings demonstrate promising results in distinguishing between high and low positive valence, achieving an F1 score of 0.65. This research opens up avenues for future research in the field of mobile mental health interventions.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-YxqGE7\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8e17dd65ad\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/dl.acm.org\/doi\/10.1145\/3565066.3608698\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?","post_excerpt":"In: MobileHCI, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"decoding-emotional-valence-from-wearables-can-our-data-reveal-our-true-feelings","to_ping":"","pinged":"","post_modified":"2024-01-18 09:30:11","post_modified_gmt":"2024-01-18 08:30:11","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14988","menu_order":30,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14933,"post_author":"5","post_date":"2024-01-16 13:19:18","post_date_gmt":"2024-01-16 12:19:18","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-1Q726J\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Paulina Adamczyk, Sylwia Marek, Ryszard Precikowski, Maciej Kus, Micha\u0142 Grzeszczyk, Maciej Malawski, Aneta Lisowska<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-CEQTHi\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-eAGB92\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">To monitor patients\u2019 well-being and evaluate the efficacy of digital health intervention, patients are required to regularly respond to standardised surveys. Responding to a large number of questionnaires is effortful and may discourage mHealth app users from engaging with the intervention. Gamification might reduce the burden of self-reporting. However, researchers have adopted various approaches to the personalisation of gamification design: ranking of game elements by the user, Hexad Gamification User Types classification (G) and selection of preferred design mockups (MU) . In this paper we report on a small population study involving 54 healthy participants aged 17 to 60, and investigate if these alternative approaches lead to the same design choices. We find that different evaluation approaches lead to different choices of gamification elements. We suggest to use game element ranking in combination with mockup selection. Hexad player classification might be less useful in the co&nbsp;ntext of mHealth applications design.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-CEQTHi\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a67432cab12\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.scitepress.org\/PublicationsDetail.aspx?ID=86vOosGEiU4=\\u0026t=1\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Designing Personalised Gamification of mHealth Survey Applications","post_excerpt":"In: Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: BIOSTEC, 224-231, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"designing-personalised-gamification-of-mhealth-survey-applications","to_ping":"","pinged":"","post_modified":"2024-02-28 17:49:03","post_modified_gmt":"2024-02-28 16:49:03","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14933","menu_order":21,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14968,"post_author":"5","post_date":"2024-01-16 13:59:08","post_date_gmt":"2024-01-16 12:59:08","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-Ctao5l\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Michal K. Grzeszczyk, Szymon P\u0142otka, Beata Rebizant, Katarzyna Kosi\u0144ska-Kaczy\u0144ska, Micha\u0142 Lipa, Robert Brawura-Biskupski-Samaha, Przemys\u0142aw Korzeniowski, Tomasz Trzci\u0144ski, Arkadiusz Sitek<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-cx4IQr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-KXYa3n\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Medical data analysis often combines both imaging and tabular data processing using machine learning algorithms. While previous studies have investigated the impact of attention mechanisms on deep learning models, few have explored integrating attention modules and tabular data. In this paper, we introduce TabAttention, a novel module that enhances the performance of Convolutional Neural Networks (CNNs) with an attention mechanism that is trained conditionally on tabular data. Specifically, we extend the Convolutional Block Attention Module to 3D by adding a Temporal Attention Module that uses multi-head self-attention to learn attention maps. Furthermore, we enhance all attention modules by integrating tabular data embeddings. Our approach is demonstrated on the fetal birth weight (FBW) estimation task, using 92 fetal abdominal ultrasound video scans and fetal biometry measurements. Our results indicate that TabAttention outperforms clinicians and existing methods that rely on tabular and\/or imaging data for FBW prediction. This novel approach has the potential to improve computer-aided diagnosis in various clinical workflows where imaging and tabular data are combined. We provide a source code for integrating TabAttention in CNNs at\u00a0<a href=\"https:\/\/github.com\/SanoScience\/Tab-Attention\">https:\/\/github.com\/SanoScience\/Tab-Attention<\/a>.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-cx4IQr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a67d89e2323\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-43990-2_33\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"TabAttention: Learning Attention Conditionally on Tabular Data","post_excerpt":"In: MICCAI, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"tabattention-learning-attention-conditionally-on-tabular-data","to_ping":"","pinged":"","post_modified":"2024-01-16 13:59:08","post_modified_gmt":"2024-01-16 12:59:08","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14968","menu_order":33,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14924,"post_author":"5","post_date":"2024-01-16 12:59:29","post_date_gmt":"2024-01-16 11:59:29","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-W4Gebu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Michal K. Grzeszczyk, Szymon P\u0142otka, Arkadiusz Sitek<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-DaNRf4\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-xVc82I\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function. The delineations of left and right ventricle blood pools and left ventricular myocardium are important for the diagnosis of cardiac diseases. Unfortunately, the movement of a patient during the CMR acquisition procedure may result in motion artifacts appearing in the final image. Such artifacts decrease the diagnostic quality of CMR images and force redoing of the procedure. In this paper, we present a Multi-task Swin UNEt TRansformer network for simultaneous solving of two tasks in the CMRxMotion challenge: CMR segmentation and motion artifacts classification. We utilize both segmentation and classification as a multi-task learning approach which allows us to determine the diagnostic quality of CMR and generate masks at the same time. CMR images are classified into three diagnostic quality classes, whereas, all samples with non-severe motion artifacts are being segmented. Ensemble of five networks trained using 5-Fold Cross-validation achieves segmentation performance of DICE coefficient of 0.871 and classification accuracy of 0.595.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-DaNRf4\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a66f8b91c7e\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2209.02470\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation","post_excerpt":"In: Statistical Atlases and Computational Modelling of the Heart Workshop (MICCAI 2022), 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"multi-task-swin-transformer-for-motion-artifacts-classification-and-cardiac-magnetic-resonance-image-segmentation","to_ping":"","pinged":"","post_modified":"2024-01-16 12:59:29","post_modified_gmt":"2024-01-16 11:59:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14924","menu_order":57,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14873,"post_author":"5","post_date":"2024-01-12 16:52:49","post_date_gmt":"2024-01-12 15:52:49","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-gryrhF\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Arkadiusz Sitek, Joanna Seliga-Siwecka, Szymon P\u0142otka, Michal K. Grzeszczyk, Szymon Seliga, Krzysztof W\u0142odarczyk, Renata Bokiniec<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-N2hnSy\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-1Sq5vk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-N2hnSy\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a160289ac79\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.nature.com\/articles\/s41390-022-02322-2\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns","post_excerpt":"In: (Nature) Pediatric Research, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"artificial-intelligence-in-the-diagnosis-of-necrotising-enterocolitis-in-newborns","to_ping":"","pinged":"","post_modified":"2024-01-12 16:52:49","post_modified_gmt":"2024-01-12 15:52:49","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14873","menu_order":59,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14847,"post_author":"5","post_date":"2024-01-10 20:40:15","post_date_gmt":"2024-01-10 19:40:15","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-O2d24a\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Jakub Zak, Michal K Grzeszczyk, Antonina Pater, Lukasz Roszkowiak, Krzysztof Siemion, Anna Korzynska<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-jt2dsT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-g1gE35\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">One of the solutions to the problem of insufficiently large training datasets in\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/image-processing\">image processing<\/a>\u00a0is data augmentation. This process artificially extends the size of training datasets to avoid overfitting. Generative Adversarial Networks yield that become increasingly difficult to differentiate from real images, until the differentiation is no longer possible. Thus, artificial images closely resembling original ones can be generated. Inclusion of artificial images contributes to improving the training process. Medical domain is one of the areas where data acquisition is burdened by many procedures, laws, and prohibitions. As a result the potential size of collected datasets is reduced. This article presents the results of training Convolutional\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/chemical-engineering\/neural-network\">Neural Networks<\/a>\u00a0on an artificially extended image datasets. The resulting\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-accuracy\">classification accuracy<\/a>\u00a0on a cell\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-task\">classification tas<\/a>k of models trained with images generated using the proposed method were increased by up to 12.9% in comparison to that of the model trained only with original dataset from the HErlev Pap smear dataset.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-jt2dsT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659ef26735636\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0208521622000675?fr=RR-2\\u0026ref=pdf_download\\u0026rr=84376252bc5bb351\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Cell image augmentation for classification task using GANs on Pap Smear Dataset","post_excerpt":"In: Biocybernetics and Biomedical Engineering Journal (IF: 5.687), 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"cell-image-augmentation-for-classification-task-using-gans-on-pap-smear-dataset","to_ping":"","pinged":"","post_modified":"2024-01-10 20:40:15","post_modified_gmt":"2024-01-10 19:40:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14847","menu_order":60,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14823,"post_author":"5","post_date":"2024-01-10 19:50:49","post_date_gmt":"2024-01-10 18:50:49","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-JIr9n4\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>P\u0142otka S, Grzeszczyk MK<\/strong>, Lipa M, Trzci\u0144ski T, and <strong>Sitek A.<\/strong><\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-FJZV8o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-oLqJsd\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during advanced pregnancy which is difficult to capture due to poor quality of images caused by the lack of amniotic fluid. As a consequence, predictions which rely on standard methods often suffer from significant errors. In this paper we propose the Residual Transformer Module which extends a 3D ResNet-based network for analysis of\u00a02D+t spatio-temporal ultrasound video scans. Our end-to-end method, called BabyNet, automatically predicts fetal birth weight based on fetal ultrasound video scans. We evaluate BabyNet using a dedicated clinical set comprising 225 2D fetal ultrasound videos of pregnancies from 75 patients performed one day prior to delivery. Experimental results show that BabyNet outperforms several state-of-the-art methods and estimates the weight at birth with accuracy comparable to human experts. Furthermore, combining estimates provided by human experts with those computed by BabyNet yields the best results, outperforming either of other methods by a significant margin. The source code of BabyNet is available at\u00a0<a href=\"https:\/\/github.com\/SanoScience\/BabyNet\">https:\/\/github.com\/SanoScience\/BabyNet<\/a>.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-FJZV8o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659ee6f317c70\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-16440-8_34\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video","post_excerpt":"In: 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"babynet-residual-transformer-module-for-birth-weight-prediction-on-fetal-ultrasound-video","to_ping":"","pinged":"","post_modified":"2024-01-10 19:50:49","post_modified_gmt":"2024-01-10 18:50:49","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=14823","menu_order":61,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12680,"post_author":"5","post_date":"2023-07-13 13:18:49","post_date_gmt":"2023-07-13 11:18:49","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-rjh03o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Magdalena Dul, Michal K. Grzeszczyk, Ewelina Nojszewska, Arkadiusz Sitek<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-PWmPt3\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-qR4EBS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This study examines the effect of COVID-19 pandemic and associated lockdowns on access to crucial diagnostic procedures for breast cancer patients, including screenings and treatments. To quantify the impact of the lockdowns on patient outcomes and cost, the study employs a mathematical model of breast cancer progression. The model includes ten different states that represent various stages of health and disease, along with the four different stages of cancer that can be diagnosed or undiagnosed. The study employs a natural history stochastic model to simulate the progression of breast cancer in patients. The model includes transition probabilities between states, estimated using both literature and empirical data. The study utilized a Markov Chain Monte Carlo simulation to model the natural history of each simulated patient over a seven-year period from 2019 to 2025. The simulation was repeated 100 times to estimate the variance in outcome variables. The study found that the COVID-19 pandemic and associated lockdowns caused a significant increase in breast cancer costs, with an average rise of 172.5 million PLN (95% CI [82.4, 262.6]) and an additional 1005 breast cancer deaths (95% CI [426, 1584]) in Poland during the simulated period. While these results are preliminary, they highlight the potential harmful impact of lockdowns on breast cancer treatment outcomes and costs.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-PWmPt3\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64c02c92909d3\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-36024-4_10\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Estimation of the Impact of COVID-19 Pandemic Lockdowns on Breast Cancer Deaths and Costs in Poland using Markovian Monte Carlo Simulation\u00a0","post_excerpt":"In: International Conference on Computational Science, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"estimation-of-the-impact-of-covid-19-pandemic-lockdowns-on-breast-cancer-deaths-and-costs-in-poland-using-markovian-monte-carlo-simulation","to_ping":"","pinged":"","post_modified":"2024-01-16 13:39:02","post_modified_gmt":"2024-01-16 12:39:02","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&#038;p=12680","menu_order":34,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12678,"post_author":"5","post_date":"2023-07-13 13:18:18","post_date_gmt":"2023-07-13 11:18:18","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-0xWdRB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>Grzeszczyk M<\/strong>K, Sat\u0142awa T, Lungu A, Swift A, Narracott A, Hose R, Trzcinski T, <strong>Sitek A<\/strong><\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-yHjDTu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-YoNxvk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Pulmonary Hypertension (PH) is a severe disease characterized by an elevated pulmonary artery pressure. The gold standard for PH diagnosis is measurement of mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart Catheterization. In this paper, we investigate noninvasive approach to PH detection utilizing Magnetic Resonance Imaging, Computer Models and Machine Learning. We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP. We compare results of regression (with thresholding of estimated mPAP) and classification and demonstrate that metrics achieved in both experiments are comparable. The predicted mPAP values are more informative to the physicians than the probability of PH returned by classification models. They provide the intuitive explanation of the outcome of the machine learning model (clinicians are accustomed to the mPAP metric, contrary to the PH probability).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-yHjDTu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64c02976587d4\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-08757-8_2\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Noninvasive Estimation of\u00a0Mean Pulmonary Artery Pressure Using MRI, Computer Models, and\u00a0Machine Learning\u00a0","post_excerpt":"In: 22nd International Conference on Computational Science, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"noninvasive-estimation-of-mean-pulmonary-artery-pressure-using-mri-computer-models-and-machine-learning","to_ping":"","pinged":"","post_modified":"2024-01-10 19:45:00","post_modified_gmt":"2024-01-10 18:45:00","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&#038;p=12678","menu_order":62,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12636,"post_author":"5","post_date":"2023-07-13 11:59:44","post_date_gmt":"2023-07-13 09:59:44","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-813KqA\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Grzeszczyk, Tadeusz A.; Grzeszczyk, Michal K.<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-y87ymG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Ja13ht\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-r9uj8H\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64afcae41e80f\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.mdpi.com\/1996-1073\/15\/5\/1852\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models\u00a0","post_excerpt":"In: Energies 2022, 15(5), 1852;, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"justifying-short-term-load-forecasts-obtained-with-the-use-of-neural-models","to_ping":"","pinged":"","post_modified":"2024-01-05 13:49:37","post_modified_gmt":"2024-01-05 12:49:37","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&#038;p=12636","menu_order":69,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12035","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":5,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12035\/revisions"}],"predecessor-version":[{"id":23143,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12035\/revisions\/23143"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/18594"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12035"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=12035"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}