Deep Learning for Medical Anomaly Detection -- A Survey

2020/12/04 by Tharindu Fernando, Harshala Gammulle, Fernando, Tharindu +7 · 31 citations
Computer Science · Engineering · Mathematics · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering #stat.ML

paper · pdf · doi:10.48550/arxiv.2012.02364

Preprint submitted to ACM Computing Surveys

arxiv created 2021/04/13 · arxiv updated 2021/04/14

Abstract

Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.

Cited by

• CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
• Group Equivariant Diffusion for Anomaly Detection in Computational Cytology
• Parallel Delayed Memory Units for Enhanced Temporal Modeling in Biomedical and Bioacoustic Signal Analysis
• SetAD: Semi-Supervised Anomaly Learning in Contextual Sets
• Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data
• Explainable Deep Convolutional Multi-Type Anomaly Detection
• QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification
• SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection
• CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments
• Anomaly Detection and Generation with Diffusion Models: A Survey
• Anomaly detection for high-content image-based phenotypic cell profiling
• Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection
• Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing
• A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)
• ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
• On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification
• Shaken or Stirred? An Analysis of MetaFormer's Token Mixing for Medical Imaging
• LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
• A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
• Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
• Anomalous Samples for Few-Shot Anomaly Detection
• Foundation Models and Transformers for Anomaly Detection: A Survey
• SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection
• Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
• PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies
• MadCLIP: Few-shot Medical Anomaly Detection with CLIP
• IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain
• Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
• SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
• Theoretical Investigation on Inductive Bias of Isolation Forest
• AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection

Related

• Deep Learning for Anomaly Detection: A Survey
• Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
• A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting
• Deep Learning for Anomaly Detection: A Review
• MedIAnomaly: A comparative study of anomaly detection in medical images