Melanoma is considered to be the deadliest variant of skin cancer causing around 75\% of total skin cancer deaths. To diagnose Melanoma, clinicians assess and compare multiple skin lesions of the same patient concurrently to gather contextual information regarding the patterns, and abnormality of the skin. So far this concurrent multi-image comparative method has not been explored by existing deep learning-based schemes. In this paper, based on contextual image feature fusion (CIFF), a deep neural network (CIFF-Net) is proposed, which integrates patient-level contextual information into the traditional approaches for improved Melanoma diagnosis by concurrent multi-image comparative method. The proposed multi-kernel self attention (MKSA) module offers better generalization of the extracted features by introducing multi-kernel operations in the self attention mechanisms. To utilize both self attention and contextual feature-wise attention, an attention guided module named contextual feature fusion (CFF) is proposed that integrates extracted features from different contextual images into a single feature vector. Finally, in comparative contextual feature fusion (CCFF) module, primary and contextual features are compared concurrently to generate comparative features. Significant improvement in performance has been achieved on the ISIC-2020 dataset over the traditional approaches that validate the effectiveness of the proposed contextual learning scheme.

翻译：黑色素瘤被认为是皮肤癌中最致命的变种，约占皮肤癌总死亡人数的75%。为诊断黑色素瘤，临床医生会同时评估并比较同一患者的多个皮肤病变，从而获取关于皮肤模式及异常特征的上下文信息。迄今为止，这一多图像并发比较方法尚未被现有基于深度学习的方案所探索。本文提出了一种基于上下文图像特征融合（CIFF）的深度神经网络（CIFF-Net），该方法将患者级别的上下文信息整合到传统方法中，通过并发多图像比较技术实现黑色素瘤诊断性能的提升。所提出的多核自注意力（MKSA）模块通过在自注意力机制中引入多核操作，增强了提取特征的泛化能力。为同时利用自注意力与上下文特征注意力，我们设计了一种名为上下文特征融合（CFF）的注意力引导模块，可将不同上下文图像中提取的特征整合为单一特征向量。最后，在比较型上下文特征融合（CCFF）模块中，通过并发比较原始特征与上下文特征生成比较特征。在ISIC-2020数据集上的实验结果表明，该方法相较传统方案取得了显著性能提升，验证了所提上下文学习方案的有效性。