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A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis by Ye, Jiarong; Xue, Yuan; Liu, Peter; Zaino, Richard; Cheng, Keith; Huang, Xiaolei is a scholarly article available to read on EtoBox.

What is A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis about?

Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable and interpretable image synthesis model is still lacking yet necessary for important applications such as assisting in medical training. In this work, we leverage the efficient self-attention and contrastive learning modules and build upon state-of-the-art generative adversarial networks (GANs) to achieve an attribute-aware image synthesis model, termed AttributeGAN, which can generate high-quality histopathology images based on multi-attribute inputs. In comparison to existing single-attribute conditional generative models, our proposed model better reflects input attributes and enables smoother interpolation among attribute values. We conduct experiments on a histopathology dataset containing stained H&E images of urothelial carcinoma and demonstrate the effectiveness of our proposed model via comprehensive quantitative and qualitative comparisons with state-of-the-art models as well as different variants of our model. Code is available at https://github.com/karenyyy/MICCAI2021AttributeGAN.

Author

Ye, Jiarong; Xue, Yuan; Liu, Peter; Zaino, Richard; Cheng, Keith; Huang, Xiaolei

Published

2021

Language

EN