Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

TL;DR

This paper introduces a knowledge-aware deep learning framework that enhances melanoma diagnosis by integrating clinical knowledge into collaborative skin lesion segmentation and recognition, improving accuracy through mutual learning.

Contribution

It proposes novel lesion-based pooling, diagnosis-guided feature fusion, and recursive mutual learning to incorporate clinical knowledge into melanoma diagnosis tasks.

Findings

Improved melanoma recognition accuracy on public datasets.

Enhanced skin lesion segmentation performance.

Effective knowledge transfer between tasks.

Abstract

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.