A U-Net Based Progressive GAN for Microscopic Image Augmentation

Qifan Zhou, Hujun Yin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Dealing with limited medical imagery data by deep neural networks is of a great concern. Obtaining large-scale labelled images requires expertise, is laborious and time consuming, and remains a challenge in medical applications. In this paper, we present a data augmentation method to cope with scarcely available medical imagery data. We propose a U-Net based generative adversarial network to synthesise microscopic images. We adopt a progressive training strategy to guide the synthesising process at multiple resolutions. This also stabilises the training process. The proposed model has been tested on three public datasets and quantitatively evaluated in terms of classification, detection and segmentation performances. Results suggest that training with the proposed augmentation method can provide significant improvements on limited and imbalanced datasets.
Original languageEnglish
Title of host publication26th UK Conference on Medical Image Understanding and Analysis (MIUA 2022)
PublisherSpringer Nature
Publication statusAccepted/In press - Jun 2022

Publication series

NameLecture Notes in Computer Science


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