Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation

Abstract

Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.

Accepted to TIP

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• A Survey on Deep Learning for Skin Lesion Segmentation
• A survey, review, and future trends of skin lesion segmentation and classification
• A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
• Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
• Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
• DONet: Dual Objective Networks for Skin Lesion Segmentation
• Object 6D Pose Estimation with Non-local Attention
• Towards Enhancing Fine-grained Details for Image Matting