Generative models in pathology: synthesis of diagnostic quality pathology images

被引:4
作者
Safarpoor, Amir [1 ]
Kalra, Shivam [1 ]
Tizhoosh, Hamid R. [1 ]
机构
[1] Univ Waterloo, Kimia Lab, 200 Univ Ave West, Waterloo, ON, Canada
关键词
deep learning; generative models; digital pathology;
D O I
10.1002/path.5577
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Within artificial intelligence and machine learning, a generative model is a powerful tool for learning any kind of data distribution. With the advent of deep learning and its success in image recognition, the field of deep generative models has clearly emerged as one of the promising fields for medical imaging. In a recent issue of The Journal of Pathology, Levine et al demonstrate the ability of generative models to synthesize high-quality pathology images. They suggested that generative models can serve as an unlimited source of images either for educating freshman pathologists or training machine learning models for diverse image analysis tasks, especially in scarce cases, while resolving patients' privacy and confidentiality concerns. (c) 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
引用
收藏
页码:131 / 132
页数:2
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