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research-article

Deep Learning for Medical Anomaly Detection – A Survey

Authors:

Tharindu Fernando,

Harshala Gammulle,

Sridha Sridharan,

Clinton FookesAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 7

Article No.: 141, Pages 1 - 37

https://doi.org/10.1145/3464423

Published: 18 July 2021 Publication History

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.

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Index Terms

Deep Learning for Medical Anomaly Detection – A Survey
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Knowledge representation and reasoning
  2. Machine learning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 7

September 2022

778 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3476825

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 18 July 2021

Accepted: 01 May 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in CSUR Volume 54, Issue 7

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