  Automatic classification of pigmented, non-pigmented, and depigmented
non-melanocytic skin lesions have garnered lots of attention in recent years.
However, imaging variations in skin texture, lesion shape, depigmentation
contrast, lighting condition, etc. hinder robust feature extraction, affecting
classification accuracy. In this paper, we propose a new deep neural network
that exploits input data for robust feature extraction. Specifically, we
analyze the convolutional network's behavior (field-of-view) to find the
location of deep supervision for improved feature extraction. To achieve this,
first, we perform activation mapping to generate an object mask, highlighting
the input regions most critical for classification output generation. Then the
network layer whose layer-wise effective receptive field matches the
approximated object shape in the object mask is selected as our focus for deep
supervision. Utilizing different types of convolutional feature extractors and
classifiers on three melanoma detection datasets and two vitiligo detection
datasets, we verify the effectiveness of our new method.
