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

arXiv: https://arxiv.org/abs/2105.11160 · HTML: https://mlsanity.com/paper/2105.11160/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices](https://mlsanity.com/paper/1912.12510/) (2019)
- [Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets](https://mlsanity.com/paper/1910.13268/) (2019)
- [Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting](https://mlsanity.com/paper/1808.01694/) (2018)
- [Skin cancer detection based on deep learning and entropy to detect outlier samples](https://mlsanity.com/paper/1909.04525/) (2019)

## Built on by (most cited first, 11 of 11)

- [Out-of-distribution Detection in Medical Image Analysis: A survey](https://mlsanity.com/paper/2404.18279/) (2024)
- [Weakly Supervised Detection of Hallucinations in LLM Activations](https://mlsanity.com/paper/2312.02798/) (2023)
- [Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets](https://mlsanity.com/paper/2305.09072/) (2023)
- [Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images](https://mlsanity.com/paper/2206.15186/) (2022)
- [Towards Fairness in AI for Melanoma Detection: Systemic Review and Recommendations](https://mlsanity.com/paper/2411.12846/) (2024)
- [Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data](https://mlsanity.com/paper/2203.04386/) (2022)
- [Domain-agnostic and Multi-level Evaluation of Generative Models](https://mlsanity.com/paper/2301.08750/) (2023)
- [Efficient Representation of the Activation Space in Deep Neural Networks](https://mlsanity.com/paper/2312.08143/) (2023)
- [Sparsity-based Feature Selection for Anomalous Subgroup Discovery](https://mlsanity.com/paper/2201.02008/) (2022)
- [A Multi-Scale Framework for Out-of-Distribution Detection in Dermoscopic Images](https://mlsanity.com/paper/2301.07533/) (2023)
- [Retrieval with Multiple Query Vectors through Anomalous Pattern Detection](https://mlsanity.com/paper/2605.01965/) (2026)

## Abstract (arXiv)

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.