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2019 | OriginalPaper | Chapter

Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss

Authors : Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Publisher: Springer International Publishing

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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.

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previous chapter Deep Multi-label Classification in Affine Subspaces
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Footnotes
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Metadata
Title
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
Authors
Mhd Hasan Sarhan
Shadi Albarqouni
Mehmet Yigitsoy
Nassir Navab
Abouzar Eslami
Copyright Year
2019
Book
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Print ISBN: 978-3-030-32238-0

Electronic ISBN: 978-3-030-32239-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-32239-7_20

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