Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
<|reference_start|>Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis: 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.<|reference_end|>
@article{ling2024agent,
    title={Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole
  Slide Image Analysis},
    author={Xitong Ling, Minxi Ouyang, Yizhi Wang, Xinrui Chen, Renao Yan, Hongbo
  Chu, Junru Cheng, Tian Guan, Sufang Tian, Xiaoping Liu, Yonghong He},
    journal={arXiv preprint arXiv:2409.11664},
    year={2024},
    doi={10.1145/3664647.3681425},
    archivePrefix={arXiv},
    eprint={2409.11664},
    primaryClass={cs.CV cs.AI}
}