Log In Sign Up

Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning

by   Hannah Kim, et al.

Recent advances in deep learning have led to breakthroughs in the development of automated skin disease classification. As we observe an increasing interest in these models in the dermatology space, it is crucial to address aspects such as the robustness towards input data distribution shifts. Current skin disease models could make incorrect inferences for test samples from different hardware devices and clinical settings or unknown disease samples, which are out-of-distribution (OOD) from the training samples. To this end, we propose a simple yet effective approach that detect these OOD samples prior to making any decision. The detection is performed via scanning in the latent space representation (e.g., activations of the inner layers of any pre-trained skin disease classifier). The input samples could also perturbed to maximise divergence of OOD samples. We validate our ODD detection approach in two use cases: 1) identify samples collected from different protocols, and 2) detect samples from unknown disease classes. Additionally, we evaluate the performance of the proposed approach and compare it with other state-of-the-art methods. Furthermore, data-driven dermatology applications may deepen the disparity in clinical care across racial and ethnic groups since most datasets are reported to suffer from bias in skin tone distribution. Therefore, we also evaluate the fairness of these OOD detection methods across different skin tones. Our experiments resulted in competitive performance across multiple datasets in detecting OOD samples, which could be used (in the future) to design more effective transfer learning techniques prior to inferring on these samples.


Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

Recent advances in computer vision and deep learning have led to breakth...

A Multi-Scale Framework for Out-of-Distribution Detection in Dermoscopic Images

The automatic detection of skin diseases via dermoscopic images can impr...

Out of distribution detection for skin and malaria images

Deep neural networks have shown promising results in disease detection a...

Out-of-Distribution Detection for Dermoscopic Image Classification

Medical image diagnosis can be achieved by deep neural networks, provide...

Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images

Recent years have witnessed a rapid development of automated methods for...

TATL: Task Agnostic Transfer Learning for Skin Attributes Detection

Existing skin attributes detection methods usually initialize with a pre...

A Smartphone-Based Skin Disease Classification Using MobileNet CNN

The MobileNet model was used by applying transfer learning on the 7 skin...