Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915475373424640 |
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| author | Xu, Shuting Liu, Runtong Chen, Zhixuan Hou, Junlin Chen, Hao |
| author_facet | Xu, Shuting Liu, Runtong Chen, Zhixuan Hou, Junlin Chen, Hao |
| contents | Deep learning has driven significant advances in mitotic figure analysis within computational pathology. In this paper, we present our approach to the Mitosis Domain Generalization (MIDOG) 2025 Challenge, which consists of two distinct tasks, i.e., mitotic figure detection and atypical mitosis classification. For the mitotic figure detection task, we propose a two-stage detection-classification framework that first localizes candidate mitotic figures and subsequently refines the predictions using a dedicated classification module. For the atypical mitosis classification task, we employ an ensemble strategy that integrates predictions from multiple state-of-the-art deep learning architectures to improve robustness and accuracy. Extensive experiments demonstrate the effectiveness of our proposed methods across both tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Xu, Shuting Liu, Runtong Chen, Zhixuan Hou, Junlin Chen, Hao Image and Video Processing Computer Vision and Pattern Recognition Deep learning has driven significant advances in mitotic figure analysis within computational pathology. In this paper, we present our approach to the Mitosis Domain Generalization (MIDOG) 2025 Challenge, which consists of two distinct tasks, i.e., mitotic figure detection and atypical mitosis classification. For the mitotic figure detection task, we propose a two-stage detection-classification framework that first localizes candidate mitotic figures and subsequently refines the predictions using a dedicated classification module. For the atypical mitosis classification task, we employ an ensemble strategy that integrates predictions from multiple state-of-the-art deep learning architectures to improve robustness and accuracy. Extensive experiments demonstrate the effectiveness of our proposed methods across both tasks. |
| title | Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02597 |