MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations

Mijung Kim, Ho-min Park, Jae Yoon Kim, Seong Hwan Kim, Sofie Hoeke, Wesley De Neve
Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR 126:292-308, 2020.

Abstract

Rotator Cuff Tears (RCTs) are a common injury among people who are middle-aged or older. For effective diagnosis of RCTs, orthopedic surgeons typically need to have access to both shoulder Magnetic Resonance Imaging (MRI) and proton density-weighted imaging. However, the generation and interpretation of such comprehensive image information is labor intensive, and thus time consuming and costly. Although computer-aided diagnosis can help in mitigating the aforementioned issues, no computational tools are currently available for diagnosing RCTs. Therefore, we introduce a computational approach towards RCT diagnosis in this paper, leveraging end-to-end learning by applying a deep convolutional neural network to shoulder MRI scans. Given that these shoulder MRI scans are 3-D by nature and highly biased towards normal shoulders, with only 6.6% of the available shoulder MRI scans containing partial-thickness tears, we made use of two tools to enhance our deep convolutional neural network. First, to enable the utilization of sequential information available in the 3-D MRI scans, we integrated a weighted linear combination layer. Second, to mitigate the presence of class imbalance, we adopted weighted cross-entropy loss. That way, we were able to obtain a diagnostic accuracy of 87% and an M-AUC score of 97%, outperforming a baseline of human annotators (diagnostic accuracy of 76% and an M-AUC score of 81%). In addition, we were able to outperform several approaches using conventional machine learning techniques. Finally, to facilitate further research efforts and ease of benchmarking, we make our dataset of 2,447 shoulder MRI scans publicly available.

Cite this Paper

BibTeX
@InProceedings{pmlr-v126-kim20a, title = {MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations}, author = {Kim, Mijung and Park, Ho-min and Kim, Jae Yoon and Kim, Seong Hwan and Hoeke, Sofie and De Neve, Wesley}, booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference}, pages = {292--308}, year = {2020}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {126}, series = {Proceedings of Machine Learning Research}, month = {07--08 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v126/kim20a/kim20a.pdf}, url = {https://proceedings.mlr.press/v126/kim20a.html}, abstract = {Rotator Cuff Tears (RCTs) are a common injury among people who are middle-aged or older. For effective diagnosis of RCTs, orthopedic surgeons typically need to have access to both shoulder Magnetic Resonance Imaging (MRI) and proton density-weighted imaging. However, the generation and interpretation of such comprehensive image information is labor intensive, and thus time consuming and costly. Although computer-aided diagnosis can help in mitigating the aforementioned issues, no computational tools are currently available for diagnosing RCTs. Therefore, we introduce a computational approach towards RCT diagnosis in this paper, leveraging end-to-end learning by applying a deep convolutional neural network to shoulder MRI scans. Given that these shoulder MRI scans are 3-D by nature and highly biased towards normal shoulders, with only 6.6% of the available shoulder MRI scans containing partial-thickness tears, we made use of two tools to enhance our deep convolutional neural network. First, to enable the utilization of sequential information available in the 3-D MRI scans, we integrated a weighted linear combination layer. Second, to mitigate the presence of class imbalance, we adopted weighted cross-entropy loss. That way, we were able to obtain a diagnostic accuracy of 87% and an M-AUC score of 97%, outperforming a baseline of human annotators (diagnostic accuracy of 76% and an M-AUC score of 81%). In addition, we were able to outperform several approaches using conventional machine learning techniques. Finally, to facilitate further research efforts and ease of benchmarking, we make our dataset of 2,447 shoulder MRI scans publicly available.} }
Endnote
%0 Conference Paper %T MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations %A Mijung Kim %A Ho-min Park %A Jae Yoon Kim %A Seong Hwan Kim %A Sofie Hoeke %A Wesley De Neve %B Proceedings of the 5th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2020 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v126-kim20a %I PMLR %P 292--308 %U https://proceedings.mlr.press/v126/kim20a.html %V 126 %X Rotator Cuff Tears (RCTs) are a common injury among people who are middle-aged or older. For effective diagnosis of RCTs, orthopedic surgeons typically need to have access to both shoulder Magnetic Resonance Imaging (MRI) and proton density-weighted imaging. However, the generation and interpretation of such comprehensive image information is labor intensive, and thus time consuming and costly. Although computer-aided diagnosis can help in mitigating the aforementioned issues, no computational tools are currently available for diagnosing RCTs. Therefore, we introduce a computational approach towards RCT diagnosis in this paper, leveraging end-to-end learning by applying a deep convolutional neural network to shoulder MRI scans. Given that these shoulder MRI scans are 3-D by nature and highly biased towards normal shoulders, with only 6.6% of the available shoulder MRI scans containing partial-thickness tears, we made use of two tools to enhance our deep convolutional neural network. First, to enable the utilization of sequential information available in the 3-D MRI scans, we integrated a weighted linear combination layer. Second, to mitigate the presence of class imbalance, we adopted weighted cross-entropy loss. That way, we were able to obtain a diagnostic accuracy of 87% and an M-AUC score of 97%, outperforming a baseline of human annotators (diagnostic accuracy of 76% and an M-AUC score of 81%). In addition, we were able to outperform several approaches using conventional machine learning techniques. Finally, to facilitate further research efforts and ease of benchmarking, we make our dataset of 2,447 shoulder MRI scans publicly available.
APA
Kim, M., Park, H., Kim, J.Y., Kim, S.H., Hoeke, S. & De Neve, W.. (2020). MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations. Proceedings of the 5th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 126:292-308 Available from https://proceedings.mlr.press/v126/kim20a.html.

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