@techreport{oai:ipsj.ixsq.nii.ac.jp:00242227,
 author = {Fengze, Li and Seiichiro, Kamata and Fengze, Li and Seiichiro, Kamata},
 issue = {1},
 month = {Jan},
 note = {Breast cancer is the most commonly diagnosed cancer in women, making it a leading cause of cancer-related deaths worldwide. Accurate diagnosis often relies on Whole-Slide Images (WSIs), a key pathological imaging modality that provides detailed visual information for identifying tumor regions. To facilitate this, advanced image segmentation methods have been developed, with convolutional neural networks (CNNs) and Transformers being the most prevalent approaches. However, CNNs often fail to capture long-range dependencies effectively, while Transformer-based methods suffer from high computational complexity. Recently, Mamba, an emerging method based on the state space model, has gained attention for its ability to address these challenges by capturing long-range dependencies with linear computational complexity. Building on this innovation, we propose the Multi-Scan Partitioning Mamba UNet for WSI segmentation. This approach integrates a novel module, the Multi-Scan Partition State Space (MPSS) module, which we introduce in detail. Extensive experiments validate the efficacy of the proposed method in processing WSIs, demonstrating its superiority over existing benchmarks., Breast cancer is the most commonly diagnosed cancer in women, making it a leading cause of cancer-related deaths worldwide. Accurate diagnosis often relies on Whole-Slide Images (WSIs), a key pathological imaging modality that provides detailed visual information for identifying tumor regions. To facilitate this, advanced image segmentation methods have been developed, with convolutional neural networks (CNNs) and Transformers being the most prevalent approaches. However, CNNs often fail to capture long-range dependencies effectively, while Transformer-based methods suffer from high computational complexity. Recently, Mamba, an emerging method based on the state space model, has gained attention for its ability to address these challenges by capturing long-range dependencies with linear computational complexity. Building on this innovation, we propose the Multi-Scan Partitioning Mamba UNet for WSI segmentation. This approach integrates a novel module, the Multi-Scan Partition State Space (MPSS) module, which we introduce in detail. Extensive experiments validate the efficacy of the proposed method in processing WSIs, demonstrating its superiority over existing benchmarks.},
 title = {Multi-scan Partitioning Visual Mamba for Breast Cancer Segmentation},
 year = {2025}
}