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Detecting Outliers with Foreign Patch Interpolation

https://doi.org/10.59275/j.melba.2022-e651
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A Stochastic Approximation Method
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Unsupervised Anomaly Localization Using Variational Auto-Encoders
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SALAD: Self-supervised Aggregation Learning for Anomaly Detection on X-Rays
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Realistic Adversarial Data Augmentation for MR Image Segmentation
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Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
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Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders
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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
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Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
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Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening
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Unsupervised lesion detection via image restoration with a normative prior
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Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study
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A review of novelty detection
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Clinically applicable deep learning for diagnosis and referral in retinal disease
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Automated abnormality classification of chest radiographs using deep convolutional neural networks
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Rethinking the Inception Architecture for Computer Vision
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Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database
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CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
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Anomaly Detection Through Latent Space Restoration Using Vector Quantized Variational Autoencoders
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
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Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
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DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
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Generative adversarial networks for brain lesion detection
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Anomaly detection for medical images based on a one-class classification
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Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
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Deep Learning for Anomaly Detection
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InfoVAE: Balancing Learning and Inference in Variational Autoencoders
inferredcites →https://doi.org/10.3389/fncom.2019.00083openalex-snapshot
Data Augmentation for Brain-Tumor Segmentation: A Review
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Unsupervised Detection of Lesions in Brain MRI using Constrained Adversarial Auto-encoders
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Adam: A Method for Stochastic Optimization
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Distilling the Knowledge in a Neural Network
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Pixel Recurrent Neural Networks
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Conditional Image Generation with PixelCNN Decoders
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Deep Anomaly Detection Using Geometric Transformations
inferredcites →https://doi.org/10.48550/arxiv.1807.03748openalex-snapshot
Representation Learning with Contrastive Predictive Coding
inferredcites →https://doi.org/10.48550/arxiv.1905.09272openalex-snapshot
Data-Efficient Image Recognition with Contrastive Predictive Coding
inferredcites →https://doi.org/10.48550/arxiv.1906.02629openalex-snapshot
When Does Label Smoothing Help?
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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
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Wide Residual Networks
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Learning Multiple Layers of Features from Tiny Images
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A Kernel Method for the Two-Sample-Problem

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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