Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection
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Abstract (arXiv)
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%
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 21 May 2022.
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References · 6
- 2018Demystifying MMD GANscited by 1,293
- 2016Revisiting Classifier Two-Sample Testscited by 268
- 2019Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Makingcited by 109
- 2019PathologyGAN: Learning deep representations of cancer tissuecited by 32
- 2017Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truthcited by 30
- 2018Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networkscited by 6
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