# Automatic lesion boundary detection in dermoscopy

## TL;DR

This paper presents a deep learning approach using U-net for automatic lesion boundary detection in dermoscopy images, aiming to improve biomedical segmentation and serve as a benchmark for future research.

## Contribution

It adapts the U-net neural network architecture for dermoscopy lesion boundary segmentation, providing a new benchmark and experimental insights for biomedical image analysis.

## Key findings

- Effective lesion boundary segmentation demonstrated
- Serves as a guideline for future boundary detection benchmarks
- Encourages further research in deep learning for biomedical segmentation

## Abstract

This manuscript addresses the problem of the automatic lesion boundary detection in dermoscopy, using deep neural networks. An approach is based on the adaptation of the U-net convolutional neural network with skip connections for lesion boundary segmentation task. I hope this paper could serve, to some extent, as an experiment of using deep convolutional networks in biomedical segmentation task and as a guideline of the boundary detection benchmark, inspiring further attempts and researches.