LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image

Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis analysis are promising directions in computational pathology. However, limited by expensive and time-consuming annotation costs, WSIs usually only have weak annotations, including 1) WSI-level Annotations (WA) and 2) Limited Patch-level Annotations (LPA). Currently, Multiple Instance Learning (MIL) often exploits WA, while LPA usually assign pseudo-labels for unlabeled data. Intuitively, pseudo-labels can serve as a practical guide for MIL, but the unreliable prediction caused by LPA inevitably introduce noise. Furthermore, WA-supervised MIL training inevitably suffers from the semantical unalignment between instances and bag-level labels. To address these problems, we design a framework called Learning from Noisy Pseudo Labels for promoting Multiple Instance Learning (LNPL-MIL), which considers both types of weak annotation. Specifically, for the LPA-trained weak classifier, we design a Super-Patch-based LNPL (SP-LNPL) method to reduce false positives in the noisy pseudo-labels and then select more accurate Top-K key instances. In MIL, we propose a Transformer aware of instance Order and Distribution (TOD-MIL) that strengthens instances correlation and weakens semantical unalignment in the bag. We validate our LNPL-MIL on Tumor Diagnosis and Survival Prediction, achieving state-of-the-art performance with at least 2.7%/2.9% AUC and 2.6%/2.3% C-Index improvement with the patches labeled for two scale. Ablation study and visualization analysis further verify the effectiveness.