论文标题
深度学习的医学图像分析的值得信赖的框架
A Trustworthy Framework for Medical Image Analysis with Deep Learning
论文作者
论文摘要
计算机视觉和机器学习在计算机辅助诊断中起着越来越重要的作用。但是，将深度学习在医学成像中的应用在数据可用性和数据不平衡方面面临着挑战，并且尤其重要的是，建立医学成像模型以值得信赖。因此，我们提出了Trudlmia，这是一个值得信赖的医学图像分析的深度学习框架，它采用模块化设计，利用自我监督的预训练，并利用一种新颖的代孕损失函数。实验评估表明，从框架产生的模型既值得信赖又表现出色。预计该框架将支持研究人员和临床医生推进使用深度学习来应对包括Covid-19在内的公共卫生危机。
Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the framework are both trustworthy and high-performing. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
