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精确瘤学的可解释计算成像:用于膀癌组织病理学诊断的可解释深度学习框架
Abdallah A Mohamed1,2, Yousry AbdulAzeem3, Abdullateef I Almudaifer4
1Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.
一个新的透明深度学习模型,YOLOv11-large,可以从基因病理学幻灯片准确地检测出膀癌 (骨髓细胞癌). 这种人工智能系统提供可靠的视觉诊断支持,改进了传统方法.
科学领域:
- * 计算机病理学
- * 在瘤学中的人工智能
- * 医学图像分析
背景情况:
- *膀癌是一个全球性的健康问题,复发率很高.
- *目前的诊断方法是侵入性的,耗时的,主观的.
- * 需要准确,高效和客观的诊断工具.
研究的目的:
- * 开发和评估用于膀癌检测的透明深度学习模型.
- *为了提高组织病理学幻灯片分析的准确性和效率.
- *为AI驱动的诊断预测提供视觉支持.
主要方法:
- *培训和测试YOLOv11深度学习架构的五个变体 (纳米,小,中,大,超大).
- *利用血素和素染色的基因病理学幻灯片的数据集,分类为炎症,尿路细胞癌 (UCC) 和无效组织.
- *采用性能指标,包括准确性,精度,回忆,AUPRC,ROC-AUC,风险覆盖分析和预期校准错误 (ECE).
主要成果:
- * YOLOv11大型模型以97.09%的精度,95.47%的精度和95.47%的回忆率实现了最高的性能.
- *使用AUPRC,ROC-AUC和风险覆盖分析进行的详细评估证实了该模型的稳定性和可靠性.
- * 该模型显示出优异的校准 (低ECE) 并确定了炎症和无效样本之间的潜在形态重叠.
结论:
- * YOLOv11大型模型代表了人工智能辅助膀癌诊断的重大进展.
- *模型的透明性质提供了视觉支持,提高了可靠性和可解释性.
- * 这种计算效率高且可扩展的AI系统更接近实际临床应用.
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