# Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/1712.10207 · HTML: https://mlsanity.com/paper/1712.10207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [Two Approaches to Supervised Image Segmentation](https://mlsanity.com/paper/2307.10123/) (2023)

## Abstract (arXiv)

One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets which outperforms state-of-the-art algorithms in the skin lesion segmentation.