Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection
This work addresses data scarcity in medical imaging for histopathology, specifically for tumor detection, but it is incremental as it applies an existing GAN method to a new domain.
The paper tackled the problem of limited histopathology data for certain cancer types by using Generative Adversarial Networks (GANs) for data augmentation, resulting in an increase in tumor detection accuracy from 80% to 87.5% after augmenting the dataset by 50%.
Advance in medical imaging is an important part in deep learning research. One of the goals of computer vision is development of a holistic, comprehensive model which can identify tumors from histology slides obtained via biopsies. A major problem that stands in the way is lack of data for a few cancer-types. In this paper, we ascertain that data augmentation using GANs can be a viable solution to reduce the unevenness in the distribution of different cancer types in our dataset. Our demonstration showed that a dataset augmented to a 50% increase causes an increase in tumor detection from 80% to 87.5%