A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. U...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Meta-Radiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950162823000036 |
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| author | Ke Zou Zhihao Chen Xuedong Yuan Xiaojing Shen Meng Wang Huazhu Fu |
| author_facet | Ke Zou Zhihao Chen Xuedong Yuan Xiaojing Shen Meng Wang Huazhu Fu |
| author_sort | Ke Zou |
| collection | DOAJ |
| description | The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician. In this paper, we review the various types of uncertainty in deep learning, including aleatoric uncertainty and epistemic uncertainty. We further discuss how they can be estimated in medical imaging. More importantly, we review recent advances in deep learning models that incorporate uncertainty estimation in medical imaging. Finally, we discuss the challenges and future directions in uncertainty estimation in deep learning for medical imaging. We hope this review will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of uncertainty estimation models in medical imaging. |
| format | Article |
| id | doaj-art-24a868886fcc47648c6e86c8b42354d7 |
| institution | Kabale University |
| issn | 2950-1628 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Meta-Radiology |
| spelling | doaj-art-24a868886fcc47648c6e86c8b42354d72024-11-12T05:22:35ZengKeAi Communications Co., Ltd.Meta-Radiology2950-16282023-06-0111100003A review of uncertainty estimation and its application in medical imagingKe Zou0Zhihao Chen1Xuedong Yuan2Xiaojing Shen3Meng Wang4Huazhu Fu5National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China; The College of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin 300350, ChinaNational Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China; The College of Computer Science, Sichuan University, Chengdu 610065, ChinaNational Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China; Department of Mathematics, Sichuan University, Chengdu 610065, ChinaInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), Singapore 138632, Republic of SingaporeInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A∗STAR), Singapore 138632, Republic of Singapore; Corresponding author.The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician. In this paper, we review the various types of uncertainty in deep learning, including aleatoric uncertainty and epistemic uncertainty. We further discuss how they can be estimated in medical imaging. More importantly, we review recent advances in deep learning models that incorporate uncertainty estimation in medical imaging. Finally, we discuss the challenges and future directions in uncertainty estimation in deep learning for medical imaging. We hope this review will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of uncertainty estimation models in medical imaging.http://www.sciencedirect.com/science/article/pii/S2950162823000036Deep learningUncertainty estimationMedical image analysis |
| spellingShingle | Ke Zou Zhihao Chen Xuedong Yuan Xiaojing Shen Meng Wang Huazhu Fu A review of uncertainty estimation and its application in medical imaging Meta-Radiology Deep learning Uncertainty estimation Medical image analysis |
| title | A review of uncertainty estimation and its application in medical imaging |
| title_full | A review of uncertainty estimation and its application in medical imaging |
| title_fullStr | A review of uncertainty estimation and its application in medical imaging |
| title_full_unstemmed | A review of uncertainty estimation and its application in medical imaging |
| title_short | A review of uncertainty estimation and its application in medical imaging |
| title_sort | review of uncertainty estimation and its application in medical imaging |
| topic | Deep learning Uncertainty estimation Medical image analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2950162823000036 |
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