Multimodal Distillation-Driven Ensemble Learning for Long-Tailed Histopathology Whole Slide Images Analysis

• URL: http://arxiv.org/abs/2503.00915v1
• Date: Sun, 02 Mar 2025 14:31:45 GMT
• Title: Multimodal Distillation-Driven Ensemble Learning for Long-Tailed Histopathology Whole Slide Images Analysis
• Authors: Xitong Ling, Yifeng Ping, Jiawen Li, Jing Peng, Yuxuan Chen, Minxi Ouyang, Yizhi Wang, Yonghong He, Tian Guan, Xiaoping Liu, Lianghui Zhu,
• Abstract summary: Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI) datasets.<n>We propose an ensemble learning method based on MIL, which employs expert decoders with shared aggregators to learn diverse distributions.<n>We introduce a multimodal distillation framework that leverages text encoders pre-trained on pathology-text pairs to distill knowledge.<n>Our method, MDE-MIL, integrates multiple expert branches focusing on specific data distributions to address long-tailed issues.
• Score: 16.01677300903562
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI) datasets. The field of WSI analysis is confronted with a severe long-tailed distribution problem, which significantly impacts the performance of classifiers. Long-tailed distributions lead to class imbalance, where some classes have sparse samples while others are abundant, making it difficult for classifiers to accurately identify minority class samples. To address this issue, we propose an ensemble learning method based on MIL, which employs expert decoders with shared aggregators and consistency constraints to learn diverse distributions and reduce the impact of class imbalance on classifier performance. Moreover, we introduce a multimodal distillation framework that leverages text encoders pre-trained on pathology-text pairs to distill knowledge and guide the MIL aggregator in capturing stronger semantic features relevant to class information. To ensure flexibility, we use learnable prompts to guide the distillation process of the pre-trained text encoder, avoiding limitations imposed by specific prompts. Our method, MDE-MIL, integrates multiple expert branches focusing on specific data distributions to address long-tailed issues. Consistency control ensures generalization across classes. Multimodal distillation enhances feature extraction. Experiments on Camelyon+-LT and PANDA-LT datasets show it outperforms state-of-the-art methods.

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