重新审视计算病理学的数据挑战：一种基于包的多实例学习训练框架
Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework
AI总结：
本文提出一种基于包的多实例学习框架，通过打包变长特征序列和引入残差分支，提升计算病理学中WSI处理的效率和准确性。
摘要：
计算病理学（CPath）将病理切片数字化为整个滑片图像（WSI），使分析成为关键的医疗任务，如癌症诊断和预后。然而，WSI具有极长的序列长度（高达200,000），显著的长度变化（从200到200,000）和有限的监督。这些极端的序列长度变化导致了高度的数据异质性和冗余。传统方法往往在训练效率和优化上做出妥协，以在有限监督下保留这种异质性。为了全面解决这些挑战，我们提出了一种基于包的MIL框架。它将多个采样、变长的特征序列打包成固定长度的序列，从而实现批量训练的同时保留数据异质性。此外，我们引入了一个残差分支，将多个切片中丢弃的特征组成一个超切片，该超切片使用定制标签进行训练。它在多切片监督的同时减轻了采样带来的特征损失。同时，引入了一个基于注意力的下采样器，用于压缩两个分支中的特征以减少冗余。通过缓解这些挑战，我们的方法在PANDA(UNI)上实现了高达8%的准确率提升，仅使用12%的训练时间。广泛的实验表明，在基础模型时代，关注CPath中的数据挑战具有显著潜力。代码是https://github.com/FangHeng/PackMIL
英文摘要：
Computational pathology (CPath) digitizes pathology slides into whole slide images (WSIs), enabling analysis for critical healthcare tasks such as cancer diagnosis and prognosis. However, WSIs possess extremely long sequence lengths (up to 200K), significant length variations (from 200 to 200K), and limited supervision. These extreme variations in sequence length lead to high data heterogeneity and redundancy. Conventional methods often compromise on training efficiency and optimization to preserve such heterogeneity under limited supervision. To comprehensively address these challenges, we propose a pack-based MIL framework. It packs multiple sampled, variable-length feature sequences into fixed-length ones, enabling batched training while preserving data heterogeneity. Moreover, we introduce a residual branch that composes discarded features from multiple slides into a hyperslide which is trained with tailored labels. It offers multi-slide supervision while mitigating feature loss from sampling. Meanwhile, an attention-driven downsampler is introduced to compress features in both branches to reduce redundancy. By alleviating these challenges, our approach achieves an accuracy improvement of up to 8% while using only 12% of the training time in the PANDA(UNI). Extensive experiments demonstrate that focusing data challenges in CPath holds significant potential in the era of foundation models. The code is https://github.com/FangHeng/PackMIL
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
