{"id":12020,"date":"2023-07-06T14:54:28","date_gmt":"2023-07-06T12:54:28","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=people&#038;p=12020"},"modified":"2025-04-23T11:59:11","modified_gmt":"2025-04-23T09:59:11","slug":"joanna-kaleta","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/joanna-kaleta\/","title":{"rendered":"Joanna Kaleta"},"excerpt":{"rendered":"<p>PhD Student in Medical Imaging and Robotics<\/p>\n","protected":false},"featured_media":12021,"template":"","people_teams":[19,35],"class_list":["post-12020","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>Joanna Kaleta - 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\/joanna-kaleta\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Joanna Kaleta\" \/>\n<meta property=\"og:description\" content=\"PhD Student in Medical Imaging and Robotics\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/joanna-kaleta\/\" \/>\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=\"2025-04-23T09:59:11+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/joanna_kaleta.png\" \/>\n\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t<meta property=\"og:image:height\" content=\"350\" \/>\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\\\/joanna-kaleta\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/joanna-kaleta\\\/\",\"name\":\"Joanna Kaleta - 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In her research, she pays special attention to projects with valuable ideas that contribute to real positive changes in the world. Previously she worked on topics related to medical signal analysis, biodiveristy and smart cities. As a PhD student at Sano in Health Informatics team she will focus on fetal image analysis and image-guided therapy using deep learning methods.<\/p>\n","email":"","social_media":false,"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":20967,"post_author":"8","post_date":"2025-01-21 16:51:16","post_date_gmt":"2025-01-21 15:51:16","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-6CqjM2\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-sabina-martyniak-joanna-kaleta-diego-dall-alba-michal-naskret-szymon-plotka-and-przemyslaw-korzeniowski\">Sabina Martyniak, Joanna Kaleta, Diego Dall'Alba, Micha\u0142 Naskr\u0119t, Szymon P\u0142otka, and Przemys\u0142aw Korzeniowski<br><\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-tI7h1k\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aU2f7H\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This work presents an innovative approach to advancing computer-assisted surgical (CAS) systems by addressing challenges in training data quality and realism.&nbsp;<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-A5sGbH\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">In this study, the authors introduce a comprehensive pipeline for creating high-quality synthetic data tailored for modern CAS systems. This pipeline integrates an advanced surgical simulator capable of generating complex annotations that surpass those found in existing public datasets. The simulator also models intricate surgical interactions, including the dynamics between instruments and deformable anatomical structures, ensuring a more realistic simulation environment.&nbsp;<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-qQq76G\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">To further enhance data realism, the researchers developed a novel image-to-image translation method based on Stable Diffusion (SD) and Low-Rank Adaptation (LoRA). This technique minimizes the visual gap between synthetic and real-world images while preserving the simulator's detailed annotations. By leveraging only a small amount of real-world data, the method enables efficient training and generalizes well to practical applications, thereby improving CAS training and guidance.&nbsp;<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-g7BCRx\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This innovative pipeline has been validated experimentally and is a significant step forward in bridging the gap between synthetic and real-world datasets for surgical applications. The dataset and code are available for the research community<a href=\"https:\/\/github.com\/SanoScience\/SimuScope\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"> github.com\/SanoScience\/SimuScope<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-tI7h1k\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-H3zZ17\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Sabina Martyniak, Joanna Kaleta, Diego Dall'Alba, Micha\u0142 Naskr\u0119t, Szymon P\u0142otka, and Przemys\u0142aw Korzeniowski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aU2f7H\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.48550\/arXiv.2412.02332&nbsp;<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-tI7h1k\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_678fc2ae54c84\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2412.02332\",\"_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\"} \/-->","post_title":"SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models","post_excerpt":"Conference manuscript in IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"simuscope-realistic-endoscopic-synthetic-dataset-generation-through-surgical-simulation-and-diffusion-models","to_ping":"","pinged":"","post_modified":"2025-05-19 21:17:07","post_modified_gmt":"2025-05-19 19:17:07","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=20967","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15038,"post_author":"5","post_date":"2024-01-18 10:24:56","post_date_gmt":"2024-01-18 09:24:56","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-G8lzyX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Joanna Kaleta, Diego Dall\u2019Alba, Szymon P\u0142otka, Przemys\u0142aw Korzeniowski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-MENX2Y\",\"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-nsQHUU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Computer-assisted surgical systems provide support information to the surgeon, which can improve the execution and overall outcome of the procedure. These systems are based on deep learning models that are trained on complex and challenging-to-annotate data. Generating synthetic data can overcome these limitations, but it is necessary to reduce the domain gap between real and synthetic data.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-MENX2Y\",\"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_65a8ee413e9c1\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/article\/10.1007\/s11548-023-03030-w?utm_source=rct_congratemailt\\u0026utm_medium=email\\u0026utm_campaign=oa_20231107\\u0026utm_content=10.1007\/s11548-023-03030-w\\u0026fbclid=IwAR2GsWKZwJLqcP5wjhvqyHlHnHeughXGMz0l4xLmub2QrXHHr6VxxQxiJ-o\",\"_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":"Minimal data requirement for realistic endoscopic image generation with Stable Diffusion","post_excerpt":"In: International Journal of Computer Assisted Radiology and Surgery, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"minimal-data-requirement-for-realistic-endoscopic-image-generation-with-stable-diffusion","to_ping":"","pinged":"","post_modified":"2024-01-18 10:24:57","post_modified_gmt":"2024-01-18 09:24:57","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=15038","menu_order":32,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":21082,"post_author":"8","post_date":"2025-02-01 14:40:31","post_date_gmt":"2025-02-01 13:40:31","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-iLlktS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-joanna-kaleta-nbsp-kacper-kania-nbsp-tomasz-trzcinski-nbsp-marek-kowalski\">Joanna Kaleta,&nbsp;Kacper Kania,&nbsp;Tomasz Trzcinski,&nbsp;Marek Kowalski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-J6a2RY\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-L9L6DE\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Separating lighting from geometry using unconstrained photo collections is a challenging task. Many previous attempts have either compromised the quality of the final output or suffered from slow training and inference speeds, raising doubts about their practicality. We introduce&nbsp;<strong>LumiGauss<\/strong>, a technique designed for 3D scene reconstruction using&nbsp;<strong>2D Gaussian Splatting<\/strong>. Our approach achieves high-quality reconstructions while enabling the synthesis of realistic lighting under novel environmental maps. This solution would be highly advantageous, as it could drastically cut down the extensive manual effort required to create sophisticated 3D assets, which often takes several days.<br>LumiGauss leverages&nbsp;<strong>precomputed radiance transfer<\/strong>, allowing seamless integration with graphics engines. Our&nbsp; representation also enables accurate&nbsp;<strong>shadow modeling<\/strong>, significantly enhancing the realism of generated renderings. We validate our method on the&nbsp;<strong>NeRF-OSR dataset<\/strong>, demonstrating its accuracy. Additionally, LumiGauss exhibits improved&nbsp;<strong>efficiency<\/strong>&nbsp;compared to baseline approaches. Access our code here:&nbsp;<a href=\"https:\/\/github.com\/joaxkal\/lumigauss\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/joaxkal\/lumigauss<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-J6a2RY\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-VoAvEk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>:&nbsp;Joanna Kaleta,&nbsp;Kacper Kania,&nbsp;Tomasz Trzcinski,&nbsp;Marek Kowalski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-VoAvEk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.48550\/arXiv.2408.04474<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-VoAvEk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: 3D reconstruction, Environmental lighting, Gaussian Splatting, Realistic lighting synthesis, Precomputed radiance transfer, NeRF-OSR dataset, LumiGauss<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-J6a2RY\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_679e23cfd68c8\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/github.com\/joaxkal\/lumigauss\",\"_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\"} \/-->\n\n<!-- wp:spacer {\"epAnimationGeneratedClass\":\"edplus_anim-J6a2RY\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->","post_title":"LumiGauss: Relightable Gaussian Splatting in the Wild","post_excerpt":"conference manuscript: https:\/\/arxiv.org\/abs\/2408.04474, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"lumigauss-relightable-gaussian-splatting-in-the-wild","to_ping":"","pinged":"","post_modified":"2025-02-21 18:12:00","post_modified_gmt":"2025-02-21 17:12:00","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=21082","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12020","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":6,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12020\/revisions"}],"predecessor-version":[{"id":23196,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12020\/revisions\/23196"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/12021"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12020"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=12020"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}