PathoHR：基于高分辨率病理图像的乳腺癌生存预测

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

在计算病理学中，乳腺癌生存预测因肿瘤异质性而面临显著挑战。例如，同一肿瘤在病理图像的不同区域可能展现出截然不同的形态学和分子特征，这使得从全切片图像（WSIs）中提取真正反映肿瘤侵袭潜力和可能生存结果的特征变得困难。本文提出PathoHR，一种新颖的乳腺癌生存预测流程，通过增强任意尺寸的病理图像，实现更有效的特征学习。我们的方法包括：（1）引入即插即用的高分辨率视觉Transformer（ViT）以提升WSI局部区域的表示能力，从而进行更细致全面的特征提取；（2）系统评估多种先进的相似性度量方法，用于比较WSI提取的特征，优化表示学习过程以更好地捕捉肿瘤特性；（3）证明遵循所提流程增强后的较小图像块，相比原始较大图像块，能达到同等或更优的预测精度，同时显著降低计算开销。实验结果验证，PathoHR通过整合增强的图像分辨率与优化的特征学习，为推进计算病理学提供了潜在途径，为更准确高效的乳腺癌生存预测指明了有前景的方向。代码将发布于https://github.com/AIGeeksGroup/PathoHR。

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.