{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-semantics-enriched-representation","title":"Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration","arxiv_id":"2007.06959","date":"2020-07-14","proceeding":null,"authors":["Fatemeh Haghighi","Mohammad Reza Hosseinzadeh Taher","Zongwei Zhou","Michael B. Gotway","Jianming Liang"],"abstract":"Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster deep semantic representation learning and yield semantically more powerful models for different medical applications. But how exactly such strong yet free semantics embedded in medical images can be harnessed for self-supervised learning remains largely unexplored. To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis. We examine our Semantic Genesis with all the publicly-available pre-trained models, by either self-supervision or fully supervision, on the six distinct target tasks, covering both classification and segmentation in various medical modalities (i.e.,CT, MRI, and X-ray). Our extensive experiments demonstrate that Semantic Genesis significantly exceeds all of its 3D counterparts as well as the de facto ImageNet-based transfer learning in 2D. This performance is attributed to our novel self-supervised learning framework, encouraging deep models to learn compelling semantic representation from abundant anatomical patterns resulting from consistent anatomies embedded in medical images. Code and pre-trained Semantic Genesis are available at https://github.com/JLiangLab/SemanticGenesis .","url_abs":"https://arxiv.org/abs/2007.06959v1","url_pdf":"https://arxiv.org/pdf/2007.06959v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-semantics-enriched-representation","repo_url":"https://github.com/JLiangLab/SemanticGenesis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"learning-semantics-enriched-representation","repo_url":"https://github.com/fhaghighi/SemanticGenesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"liver-segmentation","task_name":"Liver Segmentation"},{"task_slug":"lung-nodule-detection","task_name":"Lung Nodule Detection"},{"task_slug":"lung-nodule-segmentation","task_name":"Lung Nodule Segmentation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2018","task":"Brain Tumor Segmentation","dataset":"BRATS 2018","model":"Semantic Genesis","rank_in_archive_order":4,"of":4,"metrics":{"IoU":"68.8"},"uses_additional_data":false},{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2013","task":"Brain Tumor Segmentation","dataset":"BRATS-2013","model":"Semantic Genesis","rank_in_archive_order":1,"of":3,"metrics":{"Dice Score":"92.76"},"uses_additional_data":false},{"leaderboard":"/sota/liver-segmentation-on-lits2017","task":"Liver Segmentation","dataset":"LiTS2017","model":"Semantic Genesis","rank_in_archive_order":3,"of":9,"metrics":{"Dice":"92.27","IoU":"85.6"},"uses_additional_data":false},{"leaderboard":"/sota/lung-nodule-detection-on-luna2016-fpred","task":"Lung Nodule Detection","dataset":"LUNA2016 FPRED","model":"Semantic Genesis","rank_in_archive_order":1,"of":2,"metrics":{"AUC":"98.47"},"uses_additional_data":false},{"leaderboard":"/sota/lung-nodule-segmentation-on-lidc-idri","task":"Lung Nodule Segmentation","dataset":"LIDC-IDRI","model":"Semantic Genesis","rank_in_archive_order":2,"of":2,"metrics":{"IoU":"77.24"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2007.06959","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}