Micha\u0142 Naskr\u0119t - 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-naskret\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Micha\u0142 Naskr\u0119t\" \/>\n<meta property=\"og:description\" content=\"Scientific Programmer\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/michal-naskret\/\" \/>\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=\"2026-03-02T09:19:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/08\/michal_naskret.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\\\/michal-naskret\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/michal-naskret\\\/\",\"name\":\"Micha\u0142 Naskr\u0119t - 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His dissertation was based on his collaboration with one of the experiments at CERN in Switzerland. In the past, Micha\u0142 obtained multiple grants and scholarships including Fulbright Visiting Researcher at the University of Hawaii, and Polish NCN grants: Etiuda and Preludium. His current research focuses on applications of novel methods in computer simulations as well as training neural networks to operate robots.<\/p>\n","email":"","social_media":[{"icon":{"ID":12178,"id":12178,"title":"semantic scholar","filename":"semantic-scholar.svg","filesize":7035,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","link":"https:\/\/sano.science\/people\/irena-roterman-konieczna\/semantic-scholar\/","alt":"","author":"5","description":"","caption":"","name":"semantic-scholar","status":"inherit","uploaded_to":12176,"date":"2023-07-07 10:22:58","modified":"2023-07-07 10:22:58","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\/07\/semantic-scholar.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/mnaskret.web.cern.ch\/mnaskret\/","name":"Personal website"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"PostDoc in","link_text":"Health Informatics","text_after_link":"","link":""},"publications":[{"ID":23781,"post_author":"8","post_date":"2025-05-20 17:20:11","post_date_gmt":"2025-05-20 15:20:11","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-sHGyzO\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-diego-dall-alba-nbsp-michal-nasket-nbsp-sabina-kaminska-nbsp-przemyslaw-korzeniowski\">Diego Dall'Alba, Micha\u0142 Nasket, Sabina Kaminska, Przemys\u0142aw Korzeniowski<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-UIbTWv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-mmx73R\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Robotic-assisted surgery is evolving at a fast pace and holds significant potential for improvements through automation. Yet, methods like Reinforcement Learning (RL), which require extensive task repetition, are challenging to apply directly in real surgical scenarios due to safety and feasibility concerns. This highlights the importance of using simulated environments that combine realism with computational efficiency and scalability.<br>In response to this need, we present FF-SRL (Fast and Flexible Surgical Reinforcement Learning) \u2014 a high-speed, GPU-based simulation platform tailored for robotic surgery. Unlike traditional setups, FF-SRL runs both the physics-based simulation and the RL training process entirely on a single GPU. This design eliminates common performance limitations caused by data exchange between the CPU and GPU, significantly boosting learning speed.<br>Experimental results demonstrate that FF-SRL can decrease the training duration for intricate tasks like tissue manipulation by approximately tenfold \u2014 achieving performance in just a few minutes compared to conventional hybrid simulators. This level of efficiency opens new possibilities for testing and refining RL algorithms in surgical contexts. To support further research and collaboration, we have made the FF-SRL framework freely accessible to the research community.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-UIbTWv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-jsuB1V\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/diego-dallalba\/\">Diego Dall'Alba<\/a>, <a href=\"https:\/\/sano.science\/people\/michal-naskret\/\">Micha\u0142 Naskret<\/a>, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/Sabina-Kaminska_Sano.png\">Sabina Kaminska<\/a>, <a href=\"https:\/\/sano.science\/people\/przemyslaw-korzeniowski\/\">Przemys\u0142aw Korzeniowski<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-NEuOqB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1109\/IROS58592.2024.10801658<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-NEuOqB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Robotic-assisted surgery, Reinforcement Learning (RL), Surgical simulation, GPU-based simulation, Fast and Flexible Surgical Reinforcement Learning (FF-SRL), Real-time physics simulation, Computational efficiency, Simulation platform<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-UIbTWv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_682c9edf5049a\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2503.18616\",\"_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":"FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning","post_excerpt":"In: https:\/\/arxiv.org, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"ff-srl-high-performance-gpu-based-surgical-simulation-for-robot-learning","to_ping":"","pinged":"","post_modified":"2025-05-20 17:30:07","post_modified_gmt":"2025-05-20 15:30:07","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=23781","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"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. <\/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. <\/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. <\/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 <\/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&p=20967","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\/13728","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":8,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/13728\/revisions"}],"predecessor-version":[{"id":26547,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/13728\/revisions\/26547"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/13729"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=13728"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=13728"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}