High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis
Abstract This study proposes a semi-weakly supervised learning approach for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA) to alleviate the resource-intensive burden of exhaustive medical image annotation. Attention-based CNN-RNN models were trained on the RSNA pulmonary emboli...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01594-2 |
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| _version_ | 1849729002400382976 |
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| author | Zixuan Hu Hui Ming Lin Shobhit Mathur Robert Moreland Christopher D. Witiw Laura Jimenez-Juan Matias F. Callejas Djeven P. Deva Ervin Sejdić Errol Colak |
| author_facet | Zixuan Hu Hui Ming Lin Shobhit Mathur Robert Moreland Christopher D. Witiw Laura Jimenez-Juan Matias F. Callejas Djeven P. Deva Ervin Sejdić Errol Colak |
| author_sort | Zixuan Hu |
| collection | DOAJ |
| description | Abstract This study proposes a semi-weakly supervised learning approach for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA) to alleviate the resource-intensive burden of exhaustive medical image annotation. Attention-based CNN-RNN models were trained on the RSNA pulmonary embolism CT dataset and externally validated on a pooled dataset (Aida and FUMPE). Three configurations included weak (examination-level labels only), strong (all examination and slice-level labels), and semi-weak (examination-level labels plus a limited subset of slice-level labels). The proportion of slice-level labels varying from 0 to 100%. Notably, semi-weakly supervised models using approximately one-quarter of the total slice-level labels achieved an AUC of 0.928, closely matching the strongly supervised model’s AUC of 0.932. External validation yielded AUCs of 0.999 for the semi-weak and 1.000 for the strong model. By reducing labeling requirements without sacrificing diagnostic accuracy, this method streamlines model development, accelerates the integration of models into clinical practice, and enhances patient care. |
| format | Article |
| id | doaj-art-fd4acf6f6dc3492da32c2c1efe4c78ee |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-fd4acf6f6dc3492da32c2c1efe4c78ee2025-08-20T03:09:21ZengNature Portfolionpj Digital Medicine2398-63522025-05-01811910.1038/s41746-025-01594-2High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosisZixuan Hu0Hui Ming Lin1Shobhit Mathur2Robert Moreland3Christopher D. Witiw4Laura Jimenez-Juan5Matias F. Callejas6Djeven P. Deva7Ervin Sejdić8Errol Colak9The Edward S. Rogers Department of Electrical and Computer Engineering, University of TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoLi Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoThe Edward S. Rogers Department of Electrical and Computer Engineering, University of TorontoDepartment of Medical Imaging, St Michael’s Hospital, Unity Health TorontoAbstract This study proposes a semi-weakly supervised learning approach for pulmonary embolism (PE) detection on CT pulmonary angiography (CTPA) to alleviate the resource-intensive burden of exhaustive medical image annotation. Attention-based CNN-RNN models were trained on the RSNA pulmonary embolism CT dataset and externally validated on a pooled dataset (Aida and FUMPE). Three configurations included weak (examination-level labels only), strong (all examination and slice-level labels), and semi-weak (examination-level labels plus a limited subset of slice-level labels). The proportion of slice-level labels varying from 0 to 100%. Notably, semi-weakly supervised models using approximately one-quarter of the total slice-level labels achieved an AUC of 0.928, closely matching the strongly supervised model’s AUC of 0.932. External validation yielded AUCs of 0.999 for the semi-weak and 1.000 for the strong model. By reducing labeling requirements without sacrificing diagnostic accuracy, this method streamlines model development, accelerates the integration of models into clinical practice, and enhances patient care.https://doi.org/10.1038/s41746-025-01594-2 |
| spellingShingle | Zixuan Hu Hui Ming Lin Shobhit Mathur Robert Moreland Christopher D. Witiw Laura Jimenez-Juan Matias F. Callejas Djeven P. Deva Ervin Sejdić Errol Colak High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis npj Digital Medicine |
| title | High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis |
| title_full | High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis |
| title_fullStr | High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis |
| title_full_unstemmed | High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis |
| title_short | High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis |
| title_sort | high performance with fewer labels using semi weakly supervised learning for pulmonary embolism diagnosis |
| url | https://doi.org/10.1038/s41746-025-01594-2 |
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