基于病理基础模型特征时，更复杂的聚合器无法提升胃癌全切片图像的跨医院判别能力
With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
一项研究汇集韩国五家医院的 5,036 张胃部 H&E 染色全切片图像，在冻结的病理基础模型特征上以留一医院法比较多种多示例学习聚合器。自研的图聚合器 CHIME-MIL 平均 AUC 为 0.837，但相同配方下不超过注意力池化，配合站点对齐与双尺度融合的全局最大池化反而更优（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