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

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.

Pattern Recognition

References in corpus (6)

• Fully Convolutional Networks for Semantic Segmentation
• Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
• Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
• Few-Shot Segmentation with Global and Local Contrastive Learning
• M2IOSR: Maximal Mutual Information Open Set Recognition
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Cited by in corpus (4)

• A Survey on Deep Learning for Skin Lesion Segmentation
• A survey, review, and future trends of skin lesion segmentation and classification
• Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation
• A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends