提出新模型让病理切片端到端训练更高效准确
Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology
- 用全局相关性注意力优化稀疏注意力问题
- 在多个基准上超越两阶段主流模型，仅需不到10张3090小时
- 适合想提升病理图像分析效率的研究者
计算病理学中，预训练编码器结合多实例学习（MIL）聚合器是主流范式，但存在编码器未针对下游任务微调、与MIL解耦优化等问题。端到端（E2E）学习虽直观，却面临计算成本高、效果不佳的挑战。本文揭示了稀疏注意力导致的优化难题，并提出新型MIL模型ABMILX，通过全局相关性注意力修正与多头机制缓解该问题。结合高效的多尺度随机采样策略，基于ResNet的端到端模型在多个挑战性基准上超越现有两阶段基线模型，同时保持计算高效（<10 RTX3090小时）。结果表明端到端学习具有潜力，亟需更多关注。代码已开源。
原文摘要 · Abstract (English)
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.
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