  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.

Computer Vision and Pattern Recognition