Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single ca...
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| Published in | Progress in biomedical engineering (Bristol) Vol. 5; no. 2; pp. 22001 - 22021 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
IOP Publishing
01.04.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2516-1091 2516-1091 |
| DOI | 10.1088/2516-1091/acc2fe |
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| Abstract | The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions. |
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| AbstractList | The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions. The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions. |
| Author | Asad, Zuhayr Coburn, Lori A Wilson, Keith T Cui, Can Zhao, Shilin Landman, Bennett A Yang, Haichun Huo, Yuankai Wang, Yaohong |
| Author_xml | – sequence: 1 givenname: Can orcidid: 0000-0002-2159-5387 surname: Cui fullname: Cui, Can organization: Vanderbilt University Department of Computer Science, Nashville, TN 37235, United States of America – sequence: 2 givenname: Haichun surname: Yang fullname: Yang, Haichun organization: Vanderbilt University Medical Center Department of Pathology, Microbiology and Immunology, Nashville, TN 37215, United States of America – sequence: 3 givenname: Yaohong surname: Wang fullname: Wang, Yaohong organization: Vanderbilt University Medical Center Department of Pathology, Microbiology and Immunology, Nashville, TN 37215, United States of America – sequence: 4 givenname: Shilin surname: Zhao fullname: Zhao, Shilin organization: Vanderbilt University Medical Center Department of Biostatistics, Nashville, TN 37215, United States of America – sequence: 5 givenname: Zuhayr surname: Asad fullname: Asad, Zuhayr organization: Vanderbilt University Department of Computer Science, Nashville, TN 37235, United States of America – sequence: 6 givenname: Lori A surname: Coburn fullname: Coburn, Lori A organization: Veterans Affairs Tennessee Valley Healthcare System , Nashville, TN 37212, United States of America – sequence: 7 givenname: Keith T surname: Wilson fullname: Wilson, Keith T organization: Veterans Affairs Tennessee Valley Healthcare System , Nashville, TN 37212, United States of America – sequence: 8 givenname: Bennett A surname: Landman fullname: Landman, Bennett A organization: Vanderbilt University Department of Electrical and Computer Engineering, Nashville, TN 37235, United States of America – sequence: 9 givenname: Yuankai surname: Huo fullname: Huo, Yuankai organization: Vanderbilt University Department of Electrical and Computer Engineering, Nashville, TN 37235, United States of America |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37360402$$D View this record in MEDLINE/PubMed |
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| Keywords | multimodal learning medical data fusion method diagnosis and prognosis |
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