Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional
  Network to Learn from the Lesion
Norsang Lama R. Joe Stanley Anand Nambisan Akanksha Maurya Jason Hagerty William V. Stoecker
Deep learning implemented with convolutional network architectures can exceed
specialists' diagnostic accuracy. However, whole-image deep learning trained on
a given dataset may not generalize to other datasets. The problem arises
because extra-lesional features - ruler marks, ink marks, and other melanoma
correlates - may serve as information leaks. These extra-lesional features,
discoverable by heat maps, degrade melanoma diagnostic performance and cause
techniques learned on one data set to fail to generalize. We propose a novel
technique to improve melanoma recognition by an EfficientNet model. The model
trains the network to detect the lesion and learn features from the detected
lesion. A generalizable elliptical segmentation model for lesions was
developed, with an ellipse enclosing a lesion and the ellipse enclosed by an
extended rectangle (bounding box). The minimal bounding box was extended by 20%
to allow some background around the lesion. The publicly available
International Skin Imaging Collaboration (ISIC) 2020 skin lesion image dataset
was used to evaluate the effectiveness of the proposed method. Our test results
show that the proposed method improved diagnostic accuracy by increasing the
mean area under receiver operating characteristic curve (mean AUC) score from
0.9 to 0.922. Additionally, correctly diagnosed scores are also improved,
providing better separation of scores, thereby increasing melanoma diagnostic
confidence. The proposed lesion-focused convolutional technique warrants
further study.