{"ID":2891260,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17323","arxiv_id":"2507.17323","title":"EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations","abstract":"Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have improved optical-assisted diagnostics, they often demand extensive labeled datasets and yield \"black-box\" outputs with limited interpretability. In this paper, we propose EndoFinder, an online polyp retrieval framework that leverages multi-view scene representations for explainable and scalable CRC diagnosis. First, we develop a Polyp-aware Image Encoder by combining contrastive learning and a reconstruction task, guided by polyp segmentation masks. This self-supervised approach captures robust features without relying on large-scale annotated data. Next, we treat each polyp as a three-dimensional \"scene\" and introduce a Scene Representation Transformer, which fuses multiple views of the polyp into a single latent representation. By discretizing this representation through a hashing layer, EndoFinder enables real-time retrieval from a compiled database of historical polyp cases, where diagnostic information serves as interpretable references for new queries. We evaluate EndoFinder on both public and newly collected polyp datasets for re-identification and pathology classification. Results show that EndoFinder outperforms existing methods in accuracy while providing transparent, retrieval-based insights for clinical decision-making. By contributing a novel dataset and a scalable, explainable framework, our work addresses key challenges in polyp diagnosis and offers a promising direction for more efficient AI-driven colonoscopy workflows. The source code is available at https://github.com/ku262/EndoFinder-Scene.","short_abstract":"Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have improved optical-assisted diagnostics, they often demand extensive labeled datasets and yield \"black-box\" outputs with limited interpretabilit...","url_abs":"https://arxiv.org/abs/2507.17323","url_pdf":"https://arxiv.org/pdf/2507.17323v1","authors":"[\"Ruijie Yang\",\"Yan Zhu\",\"Peiyao Fu\",\"Yizhe Zhang\",\"Zhihua Wang\",\"Quanlin Li\",\"Pinghong Zhou\",\"Xian Yang\",\"Shuo Wang\"]","published":"2025-07-23T08:45:19Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":611863,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2891260,"paper_url":"https://arxiv.org/abs/2507.17323","paper_title":"EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations","repo_url":"https://github.com/ku262/EndoFinder-Scene","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}