Related papers: Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis

Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis

URL: http://arxiv.org/abs/2409.11664v1
Date: Wed, 18 Sep 2024 03:02:19 GMT
Title: Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
Authors: Xitong Ling, Minxi Ouyang, Yizhi Wang, Xinrui Chen, Renao Yan, Hongbo Chu, Junru Cheng, Tian Guan, Sufang Tian, Xiaoping Liu, Yonghong He,
Abstract summary: Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist pathologists in diagnosis. In weakly supervised learning, multiple instance learning (MIL) presents a promising approach for WSI classification. We propose AMD-MIL, an agent aggregator with a mask denoise mechanism.
Score: 6.708196053187949
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist pathologists in diagnosis. The gigapixel resolution of WSI and the absence of fine-grained annotations make direct classification and analysis challenging. In weakly supervised learning, multiple instance learning (MIL) presents a promising approach for WSI classification. The prevailing strategy is to use attention mechanisms to measure instance importance for classification. However, attention mechanisms fail to capture inter-instance information, and self-attention causes quadratic computational complexity. To address these challenges, we propose AMD-MIL, an agent aggregator with a mask denoise mechanism. The agent token acts as an intermediate variable between the query and key for computing instance importance. Mask and denoising matrices, mapped from agents-aggregated value, dynamically mask low-contribution representations and eliminate noise. AMD-MIL achieves better attention allocation by adjusting feature representations, capturing micro-metastases in cancer, and improving interpretability. Extensive experiments on CAMELYON-16, CAMELYON-17, TCGA-KIDNEY, and TCGA-LUNG show AMD-MIL's superiority over state-of-the-art methods.

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