重新审视计算病理学中基于切片级监督的端到端学习
Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology
AI总结：
针对计算病理学中端到端学习性能受限的问题，提出ABMILX方法，通过全局相关性注意力细化与多头机制缓解优化挑战，在多个基准上超越两阶段SOTA模型，且计算高效。
摘要：
预训练编码器用于离线特征提取，随后采用多实例学习（MIL）聚合器，已成为计算病理学（CPath）中的主导范式，有益于癌症诊断和预后。然而，由于缺乏针对下游任务的编码器微调以及与MIL的分离优化，导致性能受限。虽然切片级监督的端到端（E2E）学习是解决此问题的直观方案，但它面临高计算需求和次优结果等挑战。这些局限性促使我们重新审视E2E学习。我们认为，先前的工作忽视了固有的E2E优化挑战，导致与传统两阶段方法相比存在性能差距。在本文中，我们率先阐明了由稀疏注意力MIL引起的优化挑战，并提出了一种新颖的MIL方法，称为ABMILX。它通过基于全局相关性的注意力细化和多头机制来缓解此问题。借助高效的多尺度随机补丁采样策略，采用ABMILX的E2E训练ResNet在多个具有挑战性的基准上超越了在两阶段范式下的SOTA基础模型，同时保持计算效率（<10个RTX3090小时）。我们展示了E2E学习在CPath中的潜力，并呼吁在该领域投入更多研究关注。代码可在https://github.com/DearCaat/E2E-WSI-ABMILX获取。
英文摘要：
Pre-trained encoders for offline feature extraction followed by multiple instance learning (MIL) aggregators have become the dominant paradigm in computational pathology (CPath), benefiting cancer diagnosis and prognosis. However, performance limitations arise from the absence of encoder fine-tuning for downstream tasks and disjoint optimization with MIL. While slide-level supervised end-to-end (E2E) learning is an intuitive solution to this issue, it faces challenges such as high computational demands and suboptimal results. These limitations motivate us to revisit E2E learning. We argue that prior work neglects inherent E2E optimization challenges, leading to performance disparities compared to traditional two-stage methods. In this paper, we pioneer the elucidation of optimization challenge caused by sparse-attention MIL and propose a novel MIL called ABMILX. It mitigates this problem through global correlation-based attention refinement and multi-head mechanisms. With the efficient multi-scale random patch sampling strategy, an E2E trained ResNet with ABMILX surpasses SOTA foundation models under the two-stage paradigm across multiple challenging benchmarks, while remaining computationally efficient (<10 RTX3090 hours). We show the potential of E2E learning in CPath and calls for greater research focus in this area. The code is https://github.com/DearCaat/E2E-WSI-ABMILX.
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。
