Pseudo-label attention-based multiple instance learning for whole slide image classification

Automating disease classification in whole slide images (WSIs) is crucial for improving clinical diagnostic efficiency. However, existing multiple instance learning (MIL) approaches for this task often struggle with challenges such as insufficient focus on positive regions and data imbalance between positive and negative regions. These issues can lead to suboptimal performance in practical applications. To address these problems, in this paper, we propose a novel embedding-based MIL technique called pseudo-label attention-based multiple instance learning (PAMIL). PAMIL aggregates each instance’s features regarding their contributions to improving downstream classification performance. The key insight of PAMIL involves training the model in a supervised manner by introducing pseudo-labels to emphasize positive regions. Additionally, we propose a fine-tuning strategy to effectively refine the dataset, eliminating the interference of false-positive data and alleviating data imbalance. The effectiveness of PAMIL was demonstrated through comparisons with six state-of-the-art MIL techniques across two large-scale, real-world datasets. Empirical results show that the proposed method outperforms other methods, achieving up to a 2. 15% improvement in accuracy and a 1. 61% increase in area under the curve (AUC) on the Cancer Genome Atlas Non-Small Cell Lung Cancer (TCGA-NSCLC) dataset, highlighting the superiority of our method in practical applications, such as helping clinicians diagnose quickly.

Authors

• Jing He Xiamen University
• Ping Wang Wuhan University
• Jingwen Cai
• Dan Tang
• Shaowen Yao School of Software and AI, Yunnan University
• Renyang Liu Institute of Data Science, National University of Singapore

Keywords

• Computational pathology
• Deep learning
• Whole slide image analysis
• Weakly supervised classification
• Multiple instance learning