Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914951728201728 |
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| author | Ling, Xitong Ouyang, Minxi Wang, Yizhi Chen, Xinrui Yan, Renao Chu, Hongbo Cheng, Junru Guan, Tian Tian, Sufang Liu, Xiaoping He, Yonghong |
| author_facet | Ling, Xitong Ouyang, Minxi Wang, Yizhi Chen, Xinrui Yan, Renao Chu, Hongbo Cheng, Junru Guan, Tian Tian, Sufang Liu, Xiaoping He, Yonghong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis Ling, Xitong Ouyang, Minxi Wang, Yizhi Chen, Xinrui Yan, Renao Chu, Hongbo Cheng, Junru Guan, Tian Tian, Sufang Liu, Xiaoping He, Yonghong Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2409.11664 |