A Morphology Focused Diffusion Probabilistic Model for Synthesis of
Histopathology Images
- URL: http://arxiv.org/abs/2209.13167v2
- Date: Thu, 29 Sep 2022 02:13:12 GMT
- Title: A Morphology Focused Diffusion Probabilistic Model for Synthesis of
Histopathology Images
- Authors: Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin, Jochen
Lennerz, Stephen Yip, Hossein Farahani, Ali Bashashati
- Abstract summary: Deep learning methods have made significant advances in the analysis and classification of tissue images.
These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing.
- Score: 0.5541644538483947
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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