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.

翻译：然而,由于很难将有用的皮肤病临床知识纳入学习过程,因此,脑膜瘤诊断仍是一项具有挑战性的任务。在本文件中,我们提出一个新的知识意识深度框架,将一些临床知识纳入两项重要的脑膜瘤诊断任务的合作学习中,即皮肤腐蚀分解和黑皮瘤识别。具体地说,我们提议一个循环的相互学习机制,以进一步促进三角关系合作,从而迭代地提高皮肤损伤分解和间皮瘤识别模型的联合学习能力。实验性结果显示两种公开存在的皮肤变异数据分析方法。实验性结果显示两种公开存在的皮肤变异分析方法的有效性。实验性结果显示两种皮肤变异分析方法。