MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping

• URL: http://arxiv.org/abs/2509.02586v1
• Date: Thu, 28 Aug 2025 18:19:51 GMT
• Title: MitoDetect++: A Domain-Robust Pipeline for Mitosis Detection and Atypical Subtyping
• Authors: Esha Sadia Nasir, Jiaqi Lv, Mostafa Jahanifer, Shan E Ahmed Raza,
• Abstract summary: MitoDetect++ is a unified deep learning pipeline designed for the MIDOG 2025 challenge.<n>For detection, we employ a U-Net-based encoder-decoder architecture with EfficientNetV2-L as the backbone.<n>For classification, we leverage the Virchow2 vision transformer, fine-tuned efficiently using Low-Rank Adaptation (LoRA) to minimize resource consumption.
• Score: 5.8892536770897665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated detection and classification of mitotic figures especially distinguishing atypical from normal remain critical challenges in computational pathology. We present MitoDetect++, a unified deep learning pipeline designed for the MIDOG 2025 challenge, addressing both mitosis detection and atypical mitosis classification. For detection (Track 1), we employ a U-Net-based encoder-decoder architecture with EfficientNetV2-L as the backbone, enhanced with attention modules, and trained via combined segmentation losses. For classification (Track 2), we leverage the Virchow2 vision transformer, fine-tuned efficiently using Low-Rank Adaptation (LoRA) to minimize resource consumption. To improve generalization and mitigate domain shifts, we integrate strong augmentations, focal loss, and group-aware stratified 5-fold cross-validation. At inference, we deploy test-time augmentation (TTA) to boost robustness. Our method achieves a balanced accuracy of 0.892 across validation domains, highlighting its clinical applicability and scalability across tasks.

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