对比三种模型在前列腺癌病理图像分割中的表现，Mamba模型效果最佳。
Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO
- 采用高阶视觉状态空间与2D选择性扫描，提升多尺度病变检测能力
- 在两个数据集上均取得最高Dice分数，优于SAM和YOLO模型
- 适合医学图像分析、癌症诊断辅助系统研发人员参考
准确分割前列腺癌组织病理图像对诊断和治疗规划至关重要。本研究对三种基于深度学习的方法——Mamba、SAM和YOLO——在前列腺癌组织病理图像分割中的表现进行了对比分析。在Gleason 2019和SICAPv2两个综合性数据集上，使用Dice分数、精确率和召回率进行评估。结果表明，高阶视觉Mamba UNet（H-vmunet）模型在两个数据集上各项指标均最优。该模型通过融合高阶视觉状态空间与2D选择性扫描机制，在不同尺度下实现高效且敏感的病灶检测。研究验证了H-vmunet在临床应用中的潜力，并强调了对深度学习方法在医学图像分析中进行严谨验证与比较的重要性。研究成果有助于推动前列腺癌精准辅助诊断系统的开发。代码已公开于http://github.com/alibdz/prostate-segmentation。
原文摘要 · Abstract (English)
Accurate segmentation of prostate cancer histopathology images is crucial for diagnosis and treatment planning. This study presents a comparative analysis of three deep learning-based methods, Mamba, SAM, and YOLO, for segmenting prostate cancer histopathology images. We evaluated the performance of these models on two comprehensive datasets, Gleason 2019 and SICAPv2, using Dice score, precision, and recall metrics. Our results show that the High-order Vision Mamba UNet (H-vmunet) model outperforms the other two models, achieving the highest scores across all metrics on both datasets. The H-vmunet model's advanced architecture, which integrates high-order visual state spaces and 2D-selective-scan operations, enables efficient and sensitive lesion detection across different scales. Our study demonstrates the potential of the H-vmunet model for clinical applications and highlights the importance of robust validation and comparison of deep learning-based methods for medical image analysis. The findings of this study contribute to the development of accurate and reliable computer-aided diagnosis systems for prostate cancer. The code is available at http://github.com/alibdz/prostate-segmentation.
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