{"id":31922,"date":"2026-07-15T11:34:37","date_gmt":"2026-07-15T09:34:37","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=31922"},"modified":"2026-07-15T11:34:37","modified_gmt":"2026-07-15T09:34:37","slug":"let-me-decode-you-decoder-conditioning-with-tabular-data","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/let-me-decode-you-decoder-conditioning-with-tabular-data\/","title":{"rendered":"Let Me DeCode You: Decoder Conditioning with Tabular Data"},"content":{"rendered":"\n<p class=\"eplus-wrapper wp-block-paragraph\">Training deep learning models for 3D segmentation remains a demanding task, requiring strategies that are both computationally efficient and capable of generalizing well to unseen data. This study presents DeCode, a novel conditioning approach that leverages label-derived features to dynamically support the decoder during the reconstruction process \u2014 with the goal of improving both training efficiency and segmentation quality.<\/p>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">At the core of DeCode is the use of conditioning embeddings built from learned numerical representations of 3D label shape features. During training, these embeddings guide the network toward more robust segmentation. At inference time, when labels are unavailable, the model predicts the necessary conditioning information directly from the input, using a feed-forward network trained alongside the main model.<\/p>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">DeCode was evaluated on synthetic data and cone-beam computed tomography (CBCT) images of teeth, using three CBCT datasets \u2014 one public and two in-house. The results demonstrate that DeCode consistently outperforms unconditioned baseline models in generalization to new data, achieving higher accuracy at a lower computational cost. This work is the first to explore conditioning strategies specifically in the context of 3D segmentation, offering a more efficient way to make use of annotated training data. Code and pre-trained models are publicly available.<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\"><strong>Autors<\/strong>: Tomasz Szczepa\u0144ski, \u00a0Michal K.\u00a0 Grzeszczyk, Szymon\u00a0P\u0142otka,\u00a0Arleta\u00a0Adamowicz, Piotr\u00a0\u00a0Fudalej, Przemys\u0142aw\u00a0Korzeniowski,\u00a0Tomasz\u00a0Trzci\u0144ski, Tomasz Sitek<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n","protected":false},"excerpt":{"rendered":"<p>MICCAI, 2024<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-31922","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Let Me DeCode You: Decoder Conditioning with Tabular Data - 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\/research\/let-me-decode-you-decoder-conditioning-with-tabular-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Let Me DeCode You: Decoder Conditioning with Tabular Data\" \/>\n<meta property=\"og:description\" content=\"MICCAI, 2024\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/let-me-decode-you-decoder-conditioning-with-tabular-data\/\" \/>\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 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\\\/research\\\/let-me-decode-you-decoder-conditioning-with-tabular-data\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/let-me-decode-you-decoder-conditioning-with-tabular-data\\\/\",\"name\":\"Let Me DeCode You: Decoder Conditioning with Tabular Data - 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This study presents DeCode, a novel conditioning approach that leverages label-derived features to dynamically support the decoder during the reconstruction process \u2014 with the goal of improving both training efficiency and segmentation quality.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Training deep learning models for 3D segmentation remains a demanding task, requiring strategies that are both computationally efficient and capable of generalizing well to unseen data. 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At inference time, when labels are unavailable, the model predicts the necessary conditioning information directly from the input, using a feed-forward network trained alongside the main model.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">At the core of DeCode is the use of conditioning embeddings built from learned numerical representations of 3D label shape features. During training, these embeddings guide the network toward more robust segmentation. 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