MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis
Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In this study, we explore the concept of self-attention for tackling such subtleties in and between diseases. To this end,...
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| Veröffentlicht in: | Frontiers in medicine Jg. 8; S. 821120 |
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15.02.2022
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| Abstract | Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In this study, we explore the concept of self-attention for tackling such subtleties in and between diseases. To this end, we introduce, a multi-scale encoder-decoder self-attention (MEDUSA) mechanism tailored for medical image analysis. While self-attention deep convolutional neural network architectures in existing literature center around the notion of multiple isolated lightweight attention mechanisms with limited individual capacities being incorporated at different points in the network architecture, MEDUSA takes a significant departure from this notion by possessing a single, unified self-attention mechanism with significantly higher capacity with multiple attention heads feeding into different scales in the network architecture. To the best of the authors' knowledge, this is the first “single body, multi-scale heads” realization of self-attention and enables explicit global context among selective attention at different levels of representational abstractions while still enabling differing local attention context at individual levels of abstractions. With MEDUSA, we obtain state-of-the-art performance on multiple challenging medical image analysis benchmarks including COVIDx, Radiological Society of North America (RSNA) RICORD, and RSNA Pneumonia Challenge when compared to previous work. Our MEDUSA model is publicly available. |
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| AbstractList | Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In this study, we explore the concept of self-attention for tackling such subtleties in and between diseases. To this end, we introduce, a multi-scale encoder-decoder self-attention (MEDUSA) mechanism tailored for medical image analysis. While self-attention deep convolutional neural network architectures in existing literature center around the notion of multiple isolated lightweight attention mechanisms with limited individual capacities being incorporated at different points in the network architecture, MEDUSA takes a significant departure from this notion by possessing a single, unified self-attention mechanism with significantly higher capacity with multiple attention heads feeding into different scales in the network architecture. To the best of the authors' knowledge, this is the first “single body, multi-scale heads” realization of self-attention and enables explicit global context among selective attention at different levels of representational abstractions while still enabling differing local attention context at individual levels of abstractions. With MEDUSA, we obtain state-of-the-art performance on multiple challenging medical image analysis benchmarks including COVIDx, Radiological Society of North America (RSNA) RICORD, and RSNA Pneumonia Challenge when compared to previous work. Our MEDUSA model is publicly available. Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In this study, we explore the concept of self-attention for tackling such subtleties in and between diseases. To this end, we introduce, a multi-scale encoder-decoder self-attention (MEDUSA) mechanism tailored for medical image analysis. While self-attention deep convolutional neural network architectures in existing literature center around the notion of multiple isolated lightweight attention mechanisms with limited individual capacities being incorporated at different points in the network architecture, MEDUSA takes a significant departure from this notion by possessing a single, unified self-attention mechanism with significantly higher capacity with multiple attention heads feeding into different scales in the network architecture. To the best of the authors' knowledge, this is the first "single body, multi-scale heads" realization of self-attention and enables explicit global context among selective attention at different levels of representational abstractions while still enabling differing local attention context at individual levels of abstractions. With MEDUSA, we obtain state-of-the-art performance on multiple challenging medical image analysis benchmarks including COVIDx, Radiological Society of North America (RSNA) RICORD, and RSNA Pneumonia Challenge when compared to previous work. Our MEDUSA model is publicly available.Medical image analysis continues to hold interesting challenges given the subtle characteristics of certain diseases and the significant overlap in appearance between diseases. In this study, we explore the concept of self-attention for tackling such subtleties in and between diseases. To this end, we introduce, a multi-scale encoder-decoder self-attention (MEDUSA) mechanism tailored for medical image analysis. While self-attention deep convolutional neural network architectures in existing literature center around the notion of multiple isolated lightweight attention mechanisms with limited individual capacities being incorporated at different points in the network architecture, MEDUSA takes a significant departure from this notion by possessing a single, unified self-attention mechanism with significantly higher capacity with multiple attention heads feeding into different scales in the network architecture. To the best of the authors' knowledge, this is the first "single body, multi-scale heads" realization of self-attention and enables explicit global context among selective attention at different levels of representational abstractions while still enabling differing local attention context at individual levels of abstractions. With MEDUSA, we obtain state-of-the-art performance on multiple challenging medical image analysis benchmarks including COVIDx, Radiological Society of North America (RSNA) RICORD, and RSNA Pneumonia Challenge when compared to previous work. Our MEDUSA model is publicly available. |
| Author | Sabri, Ali Aboutalebi, Hossein Wong, Alexander Pavlova, Maya Alaref, Amer Shafiee, Mohammad Javad Gunraj, Hayden |
| AuthorAffiliation | 3 Department of Systems Design Engineering, University of Waterloo , Waterloo, ON , Canada 2 Waterloo AI Institute, University of Waterloo , Waterloo, ON , Canada 4 Department of Radiology, Niagara Health, McMaster University , Hamilton, ON , Canada 1 Department of Computer Science, University of Waterloo , Waterloo, ON , Canada 6 Department of Diagnostic Radiology, Thunder Bay Regional Health Sciences Centre , Thunder Bay, ON , Canada 5 Department of Diagnostic Imaging, Northern Ontario School of Medicine , Thunder Bay, ON , Canada |
| AuthorAffiliation_xml | – name: 5 Department of Diagnostic Imaging, Northern Ontario School of Medicine , Thunder Bay, ON , Canada – name: 3 Department of Systems Design Engineering, University of Waterloo , Waterloo, ON , Canada – name: 1 Department of Computer Science, University of Waterloo , Waterloo, ON , Canada – name: 2 Waterloo AI Institute, University of Waterloo , Waterloo, ON , Canada – name: 4 Department of Radiology, Niagara Health, McMaster University , Hamilton, ON , Canada – name: 6 Department of Diagnostic Radiology, Thunder Bay Regional Health Sciences Centre , Thunder Bay, ON , Canada |
| Author_xml | – sequence: 1 givenname: Hossein surname: Aboutalebi fullname: Aboutalebi, Hossein – sequence: 2 givenname: Maya surname: Pavlova fullname: Pavlova, Maya – sequence: 3 givenname: Hayden surname: Gunraj fullname: Gunraj, Hayden – sequence: 4 givenname: Mohammad Javad surname: Shafiee fullname: Shafiee, Mohammad Javad – sequence: 5 givenname: Ali surname: Sabri fullname: Sabri, Ali – sequence: 6 givenname: Amer surname: Alaref fullname: Alaref, Amer – sequence: 7 givenname: Alexander surname: Wong fullname: Wong, Alexander |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35242769$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1136/thoraxjnl-2017-211280 10.1186/s12880-015-0069-9 10.1038/s41598-021-89352-8 10.1007/s42979-021-00496-w 10.1097/RLI.0000000000000574 10.1038/nature14539 10.1038/s41598-020-76550-z 10.1109/ICSIPA.2017.8120663 10.1148/radiol.2020202944 10.1016/S0140-6736(20)30183-5 10.1038/s41598-019-55972-4 10.1148/radiol.2021203957 10.1038/s41598-019-48995-4 10.1038/srep46349 10.1145/3465055 10.1109/CRV.2015.25 10.1007/978-3-319-46493-0_38 10.1145/3065386 10.1148/radiol.2020201160 10.1056/NEJMoa2002032 10.1561/9781601982957 10.2174/1573405616666200406110547 10.1007/s12652-021-03464-7 10.1109/TBME.2015.2485779 10.1007/978-3-030-36802-9_25 10.1038/s41598-021-88807-2 10.3390/cancers12020390 10.3389/frai.2021.764047 10.1038/s41586-021-03922-4 |
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| Copyright | Copyright © 2022 Aboutalebi, Pavlova, Gunraj, Shafiee, Sabri, Alaref and Wong. 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Aboutalebi, Pavlova, Gunraj, Shafiee, Sabri, Alaref and Wong. 2022 Aboutalebi, Pavlova, Gunraj, Shafiee, Sabri, Alaref and Wong |
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| Keywords | COVID-19 chest X-ray (CXR) diagnosis deep neural net computer vision |
| Language | English |
| License | Copyright © 2022 Aboutalebi, Pavlova, Gunraj, Shafiee, Sabri, Alaref and Wong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: John Roubil, UMass Memorial Medical Center, United States; Ameer Elaimy, MetroWest Medical Center, United States Edited by: Thomas J. FitzGerald, UMass Memorial Health Care, United States This article was submitted to Translational Medicine, a section of the journal Frontiers in Medicine |
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| SubjectTerms | chest X-ray (CXR) computer vision COVID-19 Deep learning deep neural net diagnosis Disease Hypotheses Infections Medical imaging Medicine Neural networks Severe acute respiratory syndrome coronavirus 2 Visual perception |
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