# Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss

## TL;DR

This paper presents a novel two-stage deep learning method for microaneurysms segmentation in fundus images, utilizing multi-scale inputs and embedding triplet loss to improve accuracy in diabetic retinopathy detection.

## Contribution

It introduces a new multi-scale segmentation approach with embedding triplet loss and selective sampling, enhancing microaneurysm detection accuracy over existing methods.

## Key findings

- 30.29% relative improvement over fully convolutional neural network
- Effective multi-scale segmentation with refined classification
- Enhanced discriminative power through triplet embedding loss

## 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.