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

• URL: http://arxiv.org/abs/2301.07533v1
• Date: Wed, 18 Jan 2023 13:49:35 GMT
• Title: A Multi-Scale Framework for Out-of-Distribution Detection in Dermoscopic Images
• Authors: Zhongzheng Huang, Tao Wang, Yuanzheng Cai, Lingyu Liang
• Abstract summary: We propose a multi-scale detection framework to detect out-of-distribution skin disease image data. Our framework extracts features from different layers of the neural network. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods.
• Score: 10.20384144853726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Exploiting Diffusion Prior for Out-of-Distribution Detection [11.11093497717038]
  Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models. We present a novel approach for OOD detection that leverages the generative ability of diffusion models and the powerful feature extraction capabilities of CLIP.
  arXiv  Detail & Related papers  (2024-06-16T23:55:25Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• AnoDODE: Anomaly Detection with Diffusion ODE [0.0]
  Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. We propose a new anomaly detection method based on diffusion ODEs by estimating the density of features extracted from medical images. Our proposed method not only identifie anomalies but also provides interpretability at both the image and pixel levels.
  arXiv  Detail & Related papers  (2023-10-10T08:44:47Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• CVAD: A generic medical anomaly detector based on Cascade VAE [2.647674705784439]
  We focus on the generalizability of OOD detection for medical images and propose a self-supervised Cascade Variational autoencoder-based Anomaly Detector (CVAD) We use a variational autoencoders' cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution (ID) data. We compare the performance with the state-of-the-art deep learning models to demonstrate our model's efficacy on various open-access medical imaging datasets for both intra- and inter-class OOD.
  arXiv  Detail & Related papers  (2021-10-29T14:20:43Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Unsupervised Lesion Detection via Image Restoration with a Normative Prior [6.495883501989547]
  We propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. Experiments with gliomas and stroke lesions in brain MRI show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin.
  arXiv  Detail & Related papers  (2020-04-30T18:03:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.