{"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/a-data-efficient-deep-learning-framework-for","title":"A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images","arxiv_id":"2207.06489","date":"2022-07-13","proceeding":null,"authors":["Pranav Singh","Jacopo Cirrone"],"abstract":"The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of inflammation in autoimmune disease, holding great promise for novel insights. In this paper, we empirically develop deep learning approaches that use dermatomyositis biopsies of human tissue to detect and identify inflammatory cells. Our approach improves classification performance by 26% and segmentation performance by 5%. We also propose a novel post-processing autoencoder architecture that improves segmentation performance by an additional 3%.","url_abs":"https://arxiv.org/abs/2207.06489v5","url_pdf":"https://arxiv.org/pdf/2207.06489v5.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":"a-data-efficient-deep-learning-framework-for","repo_url":"https://github.com/pranavsinghps1/dedl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-autoimmune-dataset","task":"Classification","dataset":"Autoimmune Dataset","model":"Swin Transformer Base (Patch 4 Window 12)","rank_in_archive_order":1,"of":4,"metrics":{"F1 score":"0.891"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-autoimmune-dataset","task":"Classification","dataset":"Autoimmune Dataset","model":"VANBUREN et all","rank_in_archive_order":4,"of":4,"metrics":{"F1 score":"0.63"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-autoimmune","task":"Medical Image Segmentation","dataset":"Autoimmune Dataset","model":"Unet with APP","rank_in_archive_order":1,"of":1,"metrics":{"IoU":"0.4983"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.06489","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}