arXiv：cs.LG（机器学习，全量分类）· Luyao Tang, Yingkai Yang, Hanqi Chen, Jiewei Zheng, Chaoqi Chen, Cheng Chen·· 14 小时前AI 评分41
面向医学影像的广义生物医学发现（GBD）与 SCAN 方法
Generalized Biomedicine Discovery
AI 导读
研究提出 Generalized Biomedicine Discovery（GBD）任务及统一基准，覆盖长尾、异常与层级分类感知的发现场景，针对医学影像中扁平均衡标签假设的不足。
正文
Abstract:In real-world clinical practice, medical images face open-world shifts: (i) long-tailed rare diseases, (ii) subtle lesions dominated by normal anatomy, and (iii) hierarchical taxonomies. Yet most open-world paradigms assume flat, balanced label spaces, leaving these biomedical demands unresolved. We introduce Generalized Biomedicine Discovery (GBD) and a unified benchmark spanning long-tail, anomaly, and taxonomy-aware discovery. Our key insight is that dominant known patterns form a visual manifold that masks subtle novelty. Inspired by expert diagnosis, we propose SCAN (Surprise-evoked Complementary AccommodatioN), which follows a cognition-inspired perceptual progression: it applies predictive suppression to filter expected norms, triggers surprise-evoked salience to highlight unexpected deviations, and performs complementary accommodation to integrate these shifts into global representations. Extensive experiments show that SCAN improves novel concept discovery while generally preserving established clinical knowledge, and it plugs into existing architectures to better navigate the known-unknown trade-off in medical imaging. Code is available at this https URL.
| Comments: | Accepted by **ECCV 2026** |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00120 [cs.LG] |
| (or arXiv:2610.00120v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00120 arXiv-issued DOI via DataCite |
Submission history
From: Luyao Tang [view email]
[v1]
Wed, 9 Sep 2026 14:09:31 UTC (4,248 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org