  Melanoma is a serious form of skin cancer with high mortality rate at later
stages. Fortunately, when detected early, the prognosis of melanoma is
promising and malignant melanoma incidence rates are relatively low. As a
result, datasets are heavily imbalanced which complicates training current
state-of-the-art supervised classification AI models. We propose to use
generative models to learn the benign data distribution and detect
Out-of-Distribution (OOD) malignant images through density estimation.
Normalizing Flows (NFs) are ideal candidates for OOD detection due to their
ability to compute exact likelihoods. Nevertheless, their inductive biases
towards apparent graphical features rather than semantic context hamper
accurate OOD detection. In this work, we aim at using these biases with
domain-level knowledge of melanoma, to improve likelihood-based OOD detection
of malignant images. Our encouraging results demonstrate potential for OOD
detection of melanoma using NFs. We achieve a 9% increase in Area Under Curve
of the Receiver Operating Characteristics by using wavelet-based NFs. This
model requires significantly less parameters for inference making it more
applicable on edge devices. The proposed methodology can aid medical experts
with diagnosis of skin-cancer patients and continuously increase survival
rates. Furthermore, this research paves the way for other areas in oncology
with similar data imbalance issues.
