基于基础模型特征时，聚合器复杂度无法提升胃癌全切片图像跨医院判别能力
With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
研究用韩国五家医院的 5,036 张胃部 H&E 全切片图像（20x 与 40x 扫描），在冻结病理基础模型特征上以留一医院验证比较多实例学习聚合器。带站点对齐与双尺度融合的 CHIME-MIL 平均 AUC 为 0.837，但在匹配配方下未超过注意力池化，全局最大池化配合相同对齐与融合反而更优（AUC 0.855，P = 0.0002）。
Authors:Nheng Vanchhay, Ali Hussain, Khan Ziaullah, Moon-Il Joo, Md Ariful Islam Mozumder, Rashedul Islam Sumon, Won-Ae Lee, Min-Sun Cho, Hyeong-Chan Shin, Yujun Park, Baek Hwan Cho, Nam-Hoon Cho, Heejung An, Heung-Kook Choi, Haeyoun Kang, Hyunki Kim, Hee Cheol Kim
Abstract:Whole-slide image classifiers are usually developed within one hospital and often go untested at unseen hospitals. We assembled 5,036 haematoxylin-and-eosin-stained gastric slides from five Korean hospitals, scanned at 20x and 40x, and compared multiple-instance learning aggregators under leave-one-hospital-out validation on frozen pathology foundation-model features. A hierarchical patch-region-slide graph aggregator (CHIME-MIL) with site alignment and two-scale fusion reached a mean area under the receiver operating characteristic curve (AUC) of 0.837, above six conventional attention and transformer aggregators trained without these steps. Under a matched recipe it did not exceed attention-based pooling, and global max pooling given the same alignment and fusion was better (AUC 0.855, P = 0.0002). The alignment, which subtracts each hospital's mean feature vector without labels, onboarded a new hospital from ten unlabelled slides and cost 0.002 AUC without held-out-hospital data, but its accuracy gains were small and inconsistent. The model had low top-label calibration error (0.053) but under-confident malignancy probabilities, and removing its most-attended patches lowered the malignancy probability far more than removing random patches. With these features, more complex aggregators did not improve discrimination; what a deployable system adds is label-free onboarding, calibration checks and auditable evidence.
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.41328 [eess.IV] |
| (or arXiv:2610.41328v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41328 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Ariful Islam Mozumder [view email]
[v1]
Fri, 30 Oct 2026 06:43:47 UTC (1,057 KB)
来源：arXiv cs.AI · arxiv.org