Iterative multiple instance learning for weakly annotated whole slide image classification

Abstract

Objective. Whole slide images (WSIs) play a crucial role in histopathological analysis. The extremely high resolution of WSIs makes it laborious to obtain fine-grade annotations. Hence, classifying WSIs with only slide-level labels is often cast as a multiple instance learning (MIL) problem where a WSI is regarded as a bag and tiled into patches that are regarded as instances. The purpose of this study is to develop a novel MIL method for classifying WSIs with only slide-level labels in histopathology analysis. Approach. We propose a novel iterative MIL (IMIL) method for WSI classification where instance representations and bag representations are learned collaboratively. In particular, IMIL iteratively finetune the feature extractor with selected instances and corresponding pseudo labels generated by attention-based MIL pooling. Additionally, three procedures for robust training of IMIL are adopted: (1) the feature extractor is initialized by utilizing self-supervised learning methods on all instances, (2) samples for finetuning the feature extractor are selected according to the attention scores, and (3) a confidence-aware loss is applied for finetuning the feature extractor. Main results. Our proposed IMIL-SimCLR archives the optimal classification performance on Camelyon16 and KingMed-Lung. Compared with the baseline method CLAM, IMIL-SimCLR significantly outperforms it by 3.71% higher average area under curve (AUC) on Camelyon16 and 4.25% higher average AUC on KingMed-Lung. Additionally, our proposed IMIL-ImageNet achieve the optimal classification performance on TCGA-Lung with the average AUC of 96.55% and the accuracy of 96.76%, which significantly outperforms the baseline method CLAM by 1.65% higher average AUC and 2.09% higher average accuracy respectively. Significance. Experimental results on a public lymph node metastasis dataset, a public lung cancer diagnosis dataset and an in-house lung cancer diagnosis datasets show the effectiveness of our proposed IMIL method across different WSI classification tasks compared with other state-of-the-art MIL methods.