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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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可解释和概率意识的人工智能框架用于基于MRI的像素级膀瘤预测.

Muzammil Khan¹, Antonius G de Groot², Erik B Cornel³

¹Robotics and Mechatronics Group, University of Twente, 7522 NB, Enschede, The Netherlands. m.khan@utwente.nl.

Scientific reports

|November 19, 2025

概括

这项研究引入了一个AI框架,用于使用MRI扫描检测膀瘤 (BTs). 可解释和意识到可能性的人工智能 (ELAAI) 提高了癌症检测的准确性和透明度.

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科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

膀瘤 (BTs) 由于高复发率和进展率,存在诊断方面的挑战.
磁共振成像 (MRI) 提供了BT检测的潜力,但分析需要先进的工具.
目前用于MRI分析的人工智能 (AI) 模型在数据可用性,预测准确性和透明度方面存在局限性.

研究的目的:

开发一个可解释和概率感知AI (ELAAI) 框架,以使用MRI改进膀瘤检测.
解决现有人工智能模型的局限性,包括数据稀缺性和缺乏预测透明度.
提高AI在膀癌诊断中的可靠性和临床实用性.

主要方法:

开发了ELAAI框架,专门训练正常膀MRI扫描.
集成的MFA-Net用于多尺度特征聚合和膀细分.
整合了适应性耐受性改进步骤和SLIP-Net (视觉变换器) 与多尺度确定性不确定性 (MSDU) 头用于瘤概率预测.

主要成果:

与最先进的 (SOTA) 模型相比,ELAAI表现出更高的性能.
该框架提高了人工智能辅助临床决策的透明度和可靠性.
取得了成功的细分和膀瘤的可能性预测.

结论:

ELAAI框架提供了一种新且有效的方法,用于通过MRI通过AI辅助的膀瘤检测.
ELAAI的可解释性和概率预测能力促进了对临床应用的信任.
这种人工智能框架有可能显著改善膀恶性瘤的早期和准确检测.