{"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/dgmil-distribution-guided-multiple-instance","title":"DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification","arxiv_id":"2206.08861","date":"2022-06-17","proceeding":null,"authors":["Linhao Qu","Xiaoyuan Luo","Shaolei Liu","Manning Wang","Zhijian Song"],"abstract":"Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.","url_abs":"https://arxiv.org/abs/2206.08861v1","url_pdf":"https://arxiv.org/pdf/2206.08861v1.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":"dgmil-distribution-guided-multiple-instance","repo_url":"https://github.com/miccaiif/dgmil","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"multiple-instance-learning","task_name":"Multiple Instance Learning"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiple-instance-learning-on-camelyon16","task":"Multiple Instance Learning","dataset":"CAMELYON16","model":"DGMIL","rank_in_archive_order":14,"of":14,"metrics":{"ACC":"0.8018","AUC":"0.8368"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2206.08861","atlas_url":"https://app.syntology.ai/?focus=2206.08861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.08861"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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