Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection

• URL: http://arxiv.org/abs/2205.10691v1
• Date: Sat, 21 May 2022 23:04:20 GMT
• Title: Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection
• Authors: Vidit Gautam
• Abstract summary: 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%

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