Multi-scale microaneurysms segmentation using embedding triplet loss
Sarhan MH, Albarqouni S, Yigitsoy M, Navab N, Eslami A (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11764 LNCS
Pages Range: 174-182
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_20
Abstract
Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy progression. We introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective sampling and embedding triplet loss. The model first segments on two scales and then the segmentations are refined with a classification model. To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine. The model is evaluated quantitatively to assess the segmentation performance and qualitatively to analyze the model predictions. This approach introduces a 30.29% relative improvement over the fully convolutional neural network.
Involved external institutions
Germany (DE)How to cite
APA:
Sarhan, M.H., Albarqouni, S., Yigitsoy, M., Navab, N., & Eslami, A. (2019). Multi-scale microaneurysms segmentation using embedding triplet loss. 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. 174-182). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Sarhan, Mhd Hasan, et al. "Multi-scale microaneurysms segmentation using embedding triplet loss." 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. 174-182.
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