PathoHR：基於高解析度病理影像的乳腺癌生存預測

PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images

March 23, 2025

作者: Yang Luo, Shiru Wang, Jun Liu, Jiaxuan Xiao, Rundong Xue, Zeyu Zhang, Hao Zhang, Yu Lu, Yang Zhao, Yutong Xie

cs.AI

摘要

在計算病理學中，乳腺癌生存預測面臨著一個顯著的挑戰，即腫瘤異質性。例如，同一腫瘤在病理圖像中的不同區域可能展現出截然不同的形態學和分子特徵。這使得從全切片圖像（WSIs）中提取真正反映腫瘤侵襲潛力和可能生存結果的代表性特徵變得困難。本文介紹了PathoHR，一種新穎的乳腺癌生存預測流程，它能夠增強任何尺寸的病理圖像，從而實現更有效的特徵學習。我們的方法包括：（1）引入即插即用的高分辨率視覺Transformer（ViT）來增強WSI的局部表示，實現更細緻和全面的特徵提取；（2）系統評估多種先進的相似性度量，用於比較從WSI中提取的特徵，優化表示學習過程，以更好地捕捉腫瘤特徵；（3）展示遵循所提出流程增強後的較小圖像塊，能夠達到與原始較大圖像塊相當或更優的預測準確性，同時顯著降低計算開銷。實驗結果證實，PathoHR提供了一種將增強圖像分辨率與優化特徵學習相結合的潛在途徑，推動了計算病理學的發展，為更準確和高效的乳腺癌生存預測提供了有前景的方向。代碼將在https://github.com/AIGeeksGroup/PathoHR上提供。

English

Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morphological and molecular characteristics. This makes it difficult to extract representative features from whole slide images (WSIs) that truly reflect the tumor's aggressive potential and likely survival outcomes. In this paper, we present PathoHR, a novel pipeline for accurate breast cancer survival prediction that enhances any size of pathological images to enable more effective feature learning. Our approach entails (1) the incorporation of a plug-and-play high-resolution Vision Transformer (ViT) to enhance patch-wise WSI representation, enabling more detailed and comprehensive feature extraction, (2) the systematic evaluation of multiple advanced similarity metrics for comparing WSI-extracted features, optimizing the representation learning process to better capture tumor characteristics, (3) the demonstration that smaller image patches enhanced follow the proposed pipeline can achieve equivalent or superior prediction accuracy compared to raw larger patches, while significantly reducing computational overhead. Experimental findings valid that PathoHR provides the potential way of integrating enhanced image resolution with optimized feature learning to advance computational pathology, offering a promising direction for more accurate and efficient breast cancer survival prediction. Code will be available at https://github.com/AIGeeksGroup/PathoHR.