Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Framework
This work addresses data efficiency and heterogeneity issues in computational pathology, which is crucial for healthcare tasks like cancer diagnosis, but it is incremental as it builds on existing MIL methods with specific optimizations.
The paper tackles the challenges of computational pathology, such as extreme sequence length variations and limited supervision in whole slide images, by proposing a pack-based multiple instance learning framework that improves accuracy by up to 8% while reducing training time to 12% on the PANDA(UNI) dataset.
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