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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.07729 |
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| _version_ | 1866909218913648640 |
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| author | Sobek, Joseph Inojosa, Jose R. Medina Inojosa, Betsy J. Medina Rassoulinejad-Mousavi, S. M. Conte, Gian Marco Lopez-Jimenez, Francisco Erickson, Bradley J. |
| author_facet | Sobek, Joseph Inojosa, Jose R. Medina Inojosa, Betsy J. Medina Rassoulinejad-Mousavi, S. M. Conte, Gian Marco Lopez-Jimenez, Francisco Erickson, Bradley J. |
| contents | Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed Tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on commonly present medium and large-sized structures such as the heart, liver, and pancreas even without hyperparameter tuning. However, the models struggle with very small or rarely present structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07729 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MedYOLO: A Medical Image Object Detection Framework Sobek, Joseph Inojosa, Jose R. Medina Inojosa, Betsy J. Medina Rassoulinejad-Mousavi, S. M. Conte, Gian Marco Lopez-Jimenez, Francisco Erickson, Bradley J. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed Tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on commonly present medium and large-sized structures such as the heart, liver, and pancreas even without hyperparameter tuning. However, the models struggle with very small or rarely present structures. |
| title | MedYOLO: A Medical Image Object Detection Framework |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.07729 |