中文标题#
對抗性醫院不變特徵學習用於 WSI 切片分類
英文标题#
Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
中文摘要#
病理學基礎模型（PFMs）在全幻燈片圖像（WSI）診斷中表現出顯著的潛力。 然而，不同醫院的病理圖像由於掃描硬體和預處理風格的差異而有所不同，這可能導致 PFMs 無意中學到醫院特有的特徵，對臨床部署構成風險。 在這項工作中，我們提出了對因醫院來源特徵而產生的 PFM 領域偏差的首次系統研究。 具體而言，我們（1）構建了一個用於量化 PFM 領域偏差的流程，（2）評估和比較了多個模型的性能，並（3）提出了一種輕量級的對抗框架，從凍結的表示中移除潛在的醫院特有特徵，而無需修改編碼器本身。 通過引入一個可訓練的適配器和一個通過梯度反轉層（GRL）連接的領域分類器，我們的方法學習任務區分但領域不變的表示。 在多中心組織病理學數據集上的實驗表明，我們的方法在保持或甚至提高疾病分類性能的同時，顯著降低了領域可預測性，特別是在域外（未見過的醫院）場景中。 進一步的分析，包括醫院檢測和特徵空間可視化，證實了我們的方法在減輕醫院偏差方面的有效性。 我們將根據接受情況提供我們的代碼。
英文摘要#
Pathology foundation models (PFMs) have demonstrated remarkable potential in whole-slide image (WSI) diagnosis. However, pathology images from different hospitals often vary due to differences in scanning hardware and preprocessing styles, which may lead PFMs to inadvertently learn hospital-specific features, posing risks for clinical deployment. In this work, we present the first systematic study of domain bias in PFMs arising from hospital source characteristics. Specifically, we (1) construct a pipeline for quantifying domain bias in PFMs, (2) evaluate and compare the performance of multiple models, and (3) propose a lightweight adversarial framework that removes latent hospital-specific features from frozen representations without modifying the encoder itself. By introducing a trainable adapter and a domain classifier connected through a gradient reversal layer (GRL), our method learns task-discriminative yet domain-invariant representations. Experiments on multi-center histopathology datasets demonstrate that our approach substantially reduces domain predictability while maintaining or even improving disease classification performance, particularly in out-of-domain (unseen hospital) scenarios. Further analyses, including hospital detection and feature space visualization, confirm the effectiveness of our method in mitigating hospital bias. We will provide our code based on acceptance.
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