Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.
翻译:一个多世纪以来,病理学家对疾病组织进行视觉微观研究一直是癌症诊断和预测的基石;最近,深层学习方法在组织图象的分析和分类方面取得了显著进展;然而,在利用这些模型生成病理学图象方面开展的工作有限;这些合成图象在病理学方面有若干应用,包括教育、能力测试、隐私和数据共享方面的实用性;最近,采用了扩散概率模型来生成高质量的图象;我们首次调查了这些模型的潜在用途,以及优先形态加权和色彩正常化,以合成高质量的脑癌病理学图象。我们的详细结果显示,扩散概率模型能够将广泛的各种病理学图象合成,并且与基因对抗网络相比具有更高的性能。