Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2021
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| _version_ | 1866929309403316224 |
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| author | Lin, Emily Yuh, Esther |
| author_facet | Lin, Emily Yuh, Esther |
| contents | Purpose: To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set.
Materials and Methods: This retrospective study used semi-supervised learning to bootstrap performance. An initial "teacher" deep learning model was trained on 457 pixel-labeled head CT scans collected from one US institution from 2010-2017 and used to generate pseudo-labels on a separate unlabeled corpus of 25000 examinations from the RSNA and ASNR. A second "student" model was trained on this combined pixel- and pseudo-labeled dataset. Hyperparameter tuning was performed on a validation set of 93 scans. Testing for both classification (n=481 examinations) and segmentation (n=23 examinations, or 529 images) was performed on CQ500, a dataset of 481 scans performed in India, to evaluate out-of-distribution generalizability. The semi-supervised model was compared with a baseline model trained on only labeled data using area under the receiver operating characteristic curve (AUC), Dice similarity coefficient (DSC), and average precision (AP) metrics.
Results: The semi-supervised model achieved statistically significantly higher examination AUC on CQ500 compared with the baseline (0.939 [0.938, 0.940] vs. 0.907 [0.906, 0.908]) (p=0.009). It also achieved a higher DSC (0.829 [0.825, 0.833] vs. 0.809 [0.803, 0.812]) (p=0.012) and Pixel AP (0.848 [0.843, 0.853]) vs. 0.828 [0.817, 0.828]) compared to the baseline.
Conclusion: The addition of unlabeled data in a semi-supervised learning framework demonstrates stronger generalizability potential for intracranial hemorrhage detection and segmentation compared with a supervised baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_00582 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation Lin, Emily Yuh, Esther Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Purpose: To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set. Materials and Methods: This retrospective study used semi-supervised learning to bootstrap performance. An initial "teacher" deep learning model was trained on 457 pixel-labeled head CT scans collected from one US institution from 2010-2017 and used to generate pseudo-labels on a separate unlabeled corpus of 25000 examinations from the RSNA and ASNR. A second "student" model was trained on this combined pixel- and pseudo-labeled dataset. Hyperparameter tuning was performed on a validation set of 93 scans. Testing for both classification (n=481 examinations) and segmentation (n=23 examinations, or 529 images) was performed on CQ500, a dataset of 481 scans performed in India, to evaluate out-of-distribution generalizability. The semi-supervised model was compared with a baseline model trained on only labeled data using area under the receiver operating characteristic curve (AUC), Dice similarity coefficient (DSC), and average precision (AP) metrics. Results: The semi-supervised model achieved statistically significantly higher examination AUC on CQ500 compared with the baseline (0.939 [0.938, 0.940] vs. 0.907 [0.906, 0.908]) (p=0.009). It also achieved a higher DSC (0.829 [0.825, 0.833] vs. 0.809 [0.803, 0.812]) (p=0.012) and Pixel AP (0.848 [0.843, 0.853]) vs. 0.828 [0.817, 0.828]) compared to the baseline. Conclusion: The addition of unlabeled data in a semi-supervised learning framework demonstrates stronger generalizability potential for intracranial hemorrhage detection and segmentation compared with a supervised baseline. |
| title | Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2105.00582 |