@inproceedings{
ling2024agent,
title={Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis},
author={Xitong Ling and Minxi Ouyang and Yizhi Wang and Xinrui Chen and Renao Yan and Hongbochu and Junru Cheng and Tian Guan and Xiaoping Liu and Sufang Tian and Yonghong He},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=qQTr32f832}
}
Histopathology analysis is the gold standard for medical diagnosis.Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) level localization will assist pathologists in clinical diagnosis. With a gigapixel resolution and a scarcity of fine-grained annotations, WSI is difficult to classify directly. In the field of weakly supervised learning, multiple instance learning (MIL) serves as a promising approach to solving WSI classification tasks. Currently, a prevailing aggregation strategy is to apply attention mechanism as a measure of the importance of each instance for further classification. Notwithstanding, attention mechanism fails to capture inter-instance information and self-attention mechanism can cause quadratic computational complexity issues. To address these challenges, we propose an agent aggregator with mask denoise mechanism for multiple instance learning termed AMD-MIL. The agent token represents an intermediate variable between the query and key for implicit computation of the instance importance. Mask and denoising are also learnable matrices mapped from the agents-aggregated value, which first dynamically mask out some low-contribution instance representations and then eliminate the relative noise introduced during the mask process. AMD-MIL can indirectly achieve more reasonable attention allocation by adjusting feature representations, thereby sensitively capturing micro-metastases in cancer and achieving better interpretability. Our extensive experiments on CAMELYON-16, CAMELYON-17, TCGA-KIDNEY, and TCGA-LUNG datasets show our method’s superiority over existing state-of-the-art approaches. The code will be available upon acceptance
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
Xitong Ling Minxi Ouyang Yizhi Wang Xinrui Chen Renao Yan Hongbochu Junru Cheng Tian Guan Xiaoping Liu Sufang Tian Yonghong He