mlsanityData to 7 Oct 2026

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

arXiv 2301.07533 ↗First posted 18 Jan 2023cs.CVcited by 0

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Abstract (arXiv)

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data; in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 18 Jan 2023.

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References · 23

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11 more
  1. 2019BCN20000: Dermoscopic Lesions in the Wildcited by 224
  2. 2021Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encodingcited by 207
  3. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  4. 2021Semantically Coherent Out-of-Distribution Detectioncited by 107
  5. 2021Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditionscited by 68
  6. 2020Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Modelcited by 59
  7. 2019Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matricescited by 32
  8. 2019Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Outputcited by 20
  9. 2021Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanningcited by 11
  10. 2020Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forestcited by 6
  11. 2021Out-of-Distribution Detection for Dermoscopic Image Classificationcited by 1

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