用深度学习自动判读前列腺癌组织切片的分级，提升诊断效率和准确率。
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
- 对比YOLO、视觉Transformer和Vision Mamba三种模型在病理图像分级中的表现。
- Vision Mamba在准确率、召回率和误报率上全面领先，尤其适合临床应用。
- 该研究为前列腺癌智能诊断提供高效且精准的技术方案，适合医学AI开发者参考。
前列腺癌是影响男性健康的主要问题之一，其诊断与预后主要依赖于戈莱森评分系统。该系统需由病理专家评估前列腺组织样本并分配分级，过程耗时且高度依赖人工。为应对这一挑战，本研究评估并比较了三种深度学习方法——YOLO、视觉变压器（Vision Transformers）和Vision Mamba——在从组织病理图像中自动分类戈莱森分级方面的有效性，旨在提高前列腺癌诊疗的精准度与效率。研究使用Gleason2019和SICAPv2两个公开数据集训练并测试各模型性能，基于误报率、漏报率、精确率和召回率等指标进行评估。结果表明，Vision Mamba在各项指标上均表现最优，兼具高精确率与召回率，并显著降低误报与漏报；YOLO在速度与实时分析方面具有优势；视觉变压器擅长捕捉图像中的长程依赖关系，但计算开销较大。总体而言，Vision Mamba在准确率与计算效率间取得最佳平衡，是组织病理图像中戈莱森分级最有效的模型。
原文摘要 · Abstract (English)
Prostate cancer ranks among the leading health issues impacting men, with the Gleason scoring system serving as the primary method for diagnosis and prognosis. This system relies on expert pathologists to evaluate samples of prostate tissue and assign a Gleason grade, a task that requires significant time and manual effort. To address this challenge, artificial intelligence (AI) solutions have been explored to automate the grading process. In light of these challenges, this study evaluates and compares the effectiveness of three deep learning methodologies, YOLO, Vision Transformers, and Vision Mamba, in accurately classifying Gleason grades from histopathology images. The goal is to enhance diagnostic precision and efficiency in prostate cancer management. This study utilized two publicly available datasets, Gleason2019 and SICAPv2, to train and test the performance of YOLO, Vision Transformers, and Vision Mamba models. Each model was assessed based on its ability to classify Gleason grades accurately, considering metrics such as false positive rate, false negative rate, precision, and recall. The study also examined the computational efficiency and applicability of each method in a clinical setting. Vision Mamba demonstrated superior performance across all metrics, achieving high precision and recall rates while minimizing false positives and negatives. YOLO showed promise in terms of speed and efficiency, particularly beneficial for real-time analysis. Vision Transformers excelled in capturing long-range dependencies within images, although they presented higher computational complexity compared to the other models. Vision Mamba emerges as the most effective model for Gleason grade classification in histopathology images, offering a balance between accuracy and computational efficiency.
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