GAN-based image enrichment in digital pathology boosts segmentation accuracy
Gupta L, Klinkhammer BM, Boor P, Merhof D, Gadermayr M (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11764 LNCS
Pages Range: 631-639
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322380
DOI: 10.1007/978-3-030-32239-7_70
Abstract
We introduce the idea of ‘image enrichment’ whereby the information content of images is increased in order to enhance segmentation accuracy. Unlike in data augmentation, the focus is not on increasing the number of training samples (by adding new virtual samples), but on increasing the information for each sample. For this purpose, we use a GAN-based image-to-image translation approach to generate corresponding virtual samples from a given (original) image. The virtual samples are then merged with the original sample to create a multi-channel image, which serves as the enriched image. We train and test a segmentation network on enriched images showing kidney pathology and obtain segmentation scores exhibiting an improvement compared to conventional processing of the original images only. We perform an extensive evaluation and discuss the reasons for the improvement.
Involved external institutions
Germany (DE)How to cite
APA:
Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., & Gadermayr, M. (2019). GAN-based image enrichment in digital pathology boosts segmentation accuracy. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 631-639). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Gupta, Laxmi, et al. "GAN-based image enrichment in digital pathology boosts segmentation accuracy." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 631-639.
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