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Knowledge-aware deep framework for collaborative skin lesion segmentation and melanoma recognition
Pattern Recognition ( IF 8 ) Pub Date : 2021-06-06 , DOI: 10.1016/j.patcog.2021.108075
Xiaohong Wang , Xudong Jiang , Henghui Ding , Yuqian Zhao , Jun Liu

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.



中文翻译:

用于协作皮肤病变分割和黑色素瘤识别的知识感知深度框架

深度学习技术在皮肤科医生临床检查中表现出卓越的性能。然而,由于难以将有用的皮肤科医生临床知识纳入学习过程,黑色素瘤诊断仍然是一项具有挑战性的任务。在本文中,我们提出了一种新的知识感知深度框架,将一些临床知识整合到两个重要的黑色素瘤诊断任务的协作学习中,即皮肤病变分割和黑色素瘤识别。具体来说,为了利用病变区域和周边区域的形态学表达知识进行黑色素瘤识别,设计了基于病变的池化和形状提取(LPSE)方案,将皮肤病变分割获得的结构信息转换为黑色素瘤识别. 同时,为了将皮肤病变诊断知识从黑色素瘤识别传递到皮肤病变分割,设计了一种有效的诊断引导特征融合(DGFF)策略。此外,我们提出了一种递归互学习机制,进一步促进了任务间的协作,从而迭代地提高了模型在皮肤病变分割和黑色素瘤识别方面的联合学习能力。在两个公开可用的皮肤病变数据集上的实验结果显示了所提出的黑色素瘤分析方法的有效性。从而迭代地提高模型对皮肤病变分割和黑色素瘤识别的联合学习能力。在两个公开可用的皮肤病变数据集上的实验结果显示了所提出的黑色素瘤分析方法的有效性。从而迭代地提高模型对皮肤病变分割和黑色素瘤识别的联合学习能力。在两个公开可用的皮肤病变数据集上的实验结果显示了所提出的黑色素瘤分析方法的有效性。

更新日期:2021-07-27
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