SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

TL;DR

SkinFormer is a novel transformer-based network that effectively integrates local structural and global statistical texture features for improved skin lesion segmentation, achieving state-of-the-art results on multiple datasets.

Contribution

The paper introduces a Kurtosis-guided Statistical Counting Operator and specialized transformers to enhance texture representation in skin lesion segmentation.

Findings

Achieves 93.2% Dice score on ISIC 2018 dataset.

Outperforms existing state-of-the-art methods in skin lesion segmentation.

Demonstrates effective fusion of structural and statistical texture features.

Abstract

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a trans\textbf{Former} network (\textbf{SkinFormer}) that efficiently extracts and fuses statistical texture representation for \textbf{Skin} lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer's global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. {Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2\% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future.} Our code is available at https://github.com/Rongtao-Xu/SkinFormer.