Breast mammogram mass synthesis using GAN
Breast mass segmentation in full-field digital mammograms plays a significant role in tumour classification and treatment planning. Over the past years, deep learning methods have shown great potential in accurately segmenting the masses from mammograms. However, due to the heterogeneous nature of breast tumours, training a state-of-the-art model requires a large number of real patient datasets, which are not always accessible due to limited availability or privacy concerns.
One of the approaches to overcome the data scarcity problem is to generate synthetic images using Generative Adversarial Networks (GANs). In this project, the participants will develop a GAN model to generate synthetic mammogram masses
The four notebooks will walk you through data curation, pre-processing, GAN design and training, synthetic shape generation, and validation on a downstream task of mass segmentation. The figure below shows the overall design of the project.
