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Transformers in medical imaging: A survey

Shamshad, Fahad (författare)
MBZ Univ Artificial Intelligence, U Arab Emirates
Khan, Salman (författare)
MBZ Univ Artificial Intelligence, U Arab Emirates; Australian Natl Univ, Australia
Zamir, Syed Waqas (författare)
Incept Inst Artificial Intelligence, U Arab Emirates
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Khan, Muhammad Haris (författare)
MBZ Univ Artificial Intelligence, U Arab Emirates
Hayat, Munawar (författare)
Monash Univ, Australia
Khan, Fahad (författare)
Linköpings universitet,Datorseende,Tekniska fakulteten,MBZ Univ Artificial Intelligence, U Arab Emirates
Fu, Huazhu (författare)
ASTAR, Singapore
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 (creator_code:org_t)
ELSEVIER, 2023
2023
Engelska.
Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 88
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the fields current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Transformers; Medical image analysis; Vision transformers; Deep neural networks; Clinical report generation

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