The Role of Geometry in Convolutional Neural Networks for Medical Imaging
Convolutional neural networks (CNNs) have played an important role in medical imaging—from diagnostics to research to data integration. This has allowed clinicians to plan operations, diagnose patients earlier, and study rare diseases in more detail. However, data quality, quantity, and imbalance al...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Mayo Clinic Proceedings: Digital Health |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949761223000743 |
| _version_ | 1797237851470954496 |
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| author | Yashbir Singh, M.Tech, PhD Colleen Farrelly, MS Quincy A. Hathaway, MD, PhD Ashok Choudhary, PhD Gunnar Carlsson, PhD Bradley Erickson, MD, PhD Tim Leiner, MD, PhD |
| author_facet | Yashbir Singh, M.Tech, PhD Colleen Farrelly, MS Quincy A. Hathaway, MD, PhD Ashok Choudhary, PhD Gunnar Carlsson, PhD Bradley Erickson, MD, PhD Tim Leiner, MD, PhD |
| author_sort | Yashbir Singh, M.Tech, PhD |
| collection | DOAJ |
| description | Convolutional neural networks (CNNs) have played an important role in medical imaging—from diagnostics to research to data integration. This has allowed clinicians to plan operations, diagnose patients earlier, and study rare diseases in more detail. However, data quality, quantity, and imbalance all pose challenges for CNN training and accuracy; in addition, training costs can be high when many types of CNNs are needed in a health care system. Topology and geometry provide tools to ameliorate these challenges for CNNs when they are integrated into the CNN architecture, particularly in the data preprocessing steps or convolution layers. This paper reviews the current integration of geometric tools within CNN architectures to reduce the burden of large training datasets and offset computational costs. This paper also identifies fertile areas for future research into the integration of geometric tools with CNNs. |
| first_indexed | 2024-04-24T17:26:19Z |
| format | Article |
| id | doaj.art-ce337045c555463699e39e4f9cb3f4d6 |
| institution | Directory Open Access Journal |
| issn | 2949-7612 |
| language | English |
| last_indexed | 2024-04-24T17:26:19Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Mayo Clinic Proceedings: Digital Health |
| spelling | doaj.art-ce337045c555463699e39e4f9cb3f4d62024-03-28T06:40:52ZengElsevierMayo Clinic Proceedings: Digital Health2949-76122023-12-0114519526The Role of Geometry in Convolutional Neural Networks for Medical ImagingYashbir Singh, M.Tech, PhD0Colleen Farrelly, MS1Quincy A. Hathaway, MD, PhD2Ashok Choudhary, PhD3Gunnar Carlsson, PhD4Bradley Erickson, MD, PhD5Tim Leiner, MD, PhD6Department of Radiology, Mayo Clinic, Rochester, MNStaticlysm LLC, Miami, FLDepartment of Medical Education, West Virginia University, Morgantown, WVCenter for Individualized Medicine, Mayo Clinic, Rochester, MNDepartment of Mathematics, Stanford University, Stanford, CADepartment of Radiology, Mayo Clinic, Rochester, MNDepartment of Radiology, Mayo Clinic, Rochester, MN; Correspondence: Address to Tim Leiner, MD, PhD, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905.Convolutional neural networks (CNNs) have played an important role in medical imaging—from diagnostics to research to data integration. This has allowed clinicians to plan operations, diagnose patients earlier, and study rare diseases in more detail. However, data quality, quantity, and imbalance all pose challenges for CNN training and accuracy; in addition, training costs can be high when many types of CNNs are needed in a health care system. Topology and geometry provide tools to ameliorate these challenges for CNNs when they are integrated into the CNN architecture, particularly in the data preprocessing steps or convolution layers. This paper reviews the current integration of geometric tools within CNN architectures to reduce the burden of large training datasets and offset computational costs. This paper also identifies fertile areas for future research into the integration of geometric tools with CNNs.http://www.sciencedirect.com/science/article/pii/S2949761223000743 |
| spellingShingle | Yashbir Singh, M.Tech, PhD Colleen Farrelly, MS Quincy A. Hathaway, MD, PhD Ashok Choudhary, PhD Gunnar Carlsson, PhD Bradley Erickson, MD, PhD Tim Leiner, MD, PhD The Role of Geometry in Convolutional Neural Networks for Medical Imaging Mayo Clinic Proceedings: Digital Health |
| title | The Role of Geometry in Convolutional Neural Networks for Medical Imaging |
| title_full | The Role of Geometry in Convolutional Neural Networks for Medical Imaging |
| title_fullStr | The Role of Geometry in Convolutional Neural Networks for Medical Imaging |
| title_full_unstemmed | The Role of Geometry in Convolutional Neural Networks for Medical Imaging |
| title_short | The Role of Geometry in Convolutional Neural Networks for Medical Imaging |
| title_sort | role of geometry in convolutional neural networks for medical imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2949761223000743 |
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