With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
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Nheng Vanchhay, Ali Hussain, Khan Ziaullah, Moon-Il Joo, Md Ariful Islam Mozumder, Rashedul Islam Sumon et al. · arXiv: 2610.41328v1 · Summary page
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In pathology, simpler aggregation holds its ground
Whole-slide pathology classifiers must combine information from many image patches, and increasingly elaborate architectures promise better ways to do so. A preprint using 5,036 gastric slides from five Korean hospitals tests that promise by holding out hospitals in turn. With features supplied by a frozen pathology foundation model, a hierarchical graph-based aggregator initially outperforms conventional alternatives—but loses that advantage when the comparison gives competitors the same feature alignment and multiscale fusion steps. Under that matched recipe, global max pooling performs better, reaching an AUC of 0.855 against 0.837 for the graph-based system.
The useful lesson is not that model design never matters, but that supporting procedures can be mistaken for architectural progress. The study also examines deployment details: label-free alignment can onboard a hospital using ten unlabelled slides, although its accuracy gains are small and inconsistent. Calibration checks reveal under-confident malignancy probabilities, while removing highly attended patches changes predictions more than removing random patches. Together, these tests shift attention from complexity alone toward fair comparisons, adaptation to unfamiliar hospitals and inspectable evidence.
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