Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network

TL;DR

This paper introduces a dilated scale-wise feature fusion network that improves skin lesion segmentation accuracy in dermoscopic images, especially under challenging conditions like indistinct boundaries and low contrast.

Contribution

The paper presents a novel convolution factorization-based network that extracts and fuses multi-scale features for enhanced lesion detection, outperforming existing models.

Findings

Achieves state-of-the-art segmentation accuracy

Demonstrates robustness in challenging imaging conditions

Offers efficient and effective lesion detection

Abstract

Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches show poor performance in challenging circumstances such as indistinct lesion boundaries, low contrast between the lesion and the surrounding area, or heterogeneous background that causes over/under segmentation of the skin lesion. To accurately recognize the lesion from the neighboring regions, we propose a dilated scale-wise feature fusion network based on convolution factorization. Our network is designed to simultaneously extract features at different scales which are systematically fused for better detection. The proposed model has satisfactory accuracy and efficiency. Various experiments for lesion segmentation are performed along with comparisons with the state-of-the-art models. Our proposed model consistently showcases state-of-the-art results.