Labeling of Multilingual Breast MRI Reports

Chen Han Tsai; Nahum Kiryati; Eli Konen; Miri Sklair-Levy; Arnaldo Mayer

doi:10.1007/978-3-030-61166-8_25

Labeling of Multilingual Breast MRI Reports

Chen Han Tsai^*, Nahum Kiryati, Eli Konen, Miri Sklair-Levy, Arnaldo Mayer

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Medical reports are an essential medium in recording a patient’s condition throughout a clinical trial. They contain valuable information that can be extracted to generate a large labeled dataset needed for the development of clinical tools. However, the majority of medical reports are stored in an unregularized format, and a trained human annotator (typically a doctor) must manually assess and label each case, resulting in an expensive and time consuming procedure. In this work, we present a framework for developing a multilingual breast MRI report classifier using a custom-built language representation called LAMBR. Our proposed method overcomes practical challenges faced in clinical settings, and we demonstrate improved performance in extracting labels from medical reports when compared with conventional approaches.

Original language	English
Title of host publication	Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Jaime Cardoso, Wilson Silva, Ricardo Cruz, Hien Van Nguyen, Badri Roysam, Nicholas Heller, Pedro Henriques Abreu, Jose Pereira Amorim, Ivana Isgum, Vishal Patel, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Samaneh Abbasi, Diana Mateus, Emanuele Trucco
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	233-241
Number of pages	9
ISBN (Print)	9783030611651
DOIs	https://doi.org/10.1007/978-3-030-61166-8_25
State	Published - 2020
Event	3rd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the 2nd International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12446 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the 2nd International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/20 → 8/10/20

Keywords

Breast MRI
LAMBR
Labeling
Medical reports
Transfer learning

Access to Document

10.1007/978-3-030-61166-8_25

Cite this

Tsai, C. H., Kiryati, N., Konen, E., Sklair-Levy, M., & Mayer, A. (2020). Labeling of Multilingual Breast MRI Reports. In J. Cardoso, W. Silva, R. Cruz, H. Van Nguyen, B. Roysam, N. Heller, P. Henriques Abreu, J. Pereira Amorim, I. Isgum, V. Patel, K. Zhou, S. Jiang, N. Le, K. Luu, R. Sznitman, V. Cheplygina, S. Abbasi, D. Mateus, & E. Trucco (Eds.), Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 233-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12446 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61166-8_25

Tsai, Chen Han ; Kiryati, Nahum ; Konen, Eli et al. / Labeling of Multilingual Breast MRI Reports. Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Jaime Cardoso ; Wilson Silva ; Ricardo Cruz ; Hien Van Nguyen ; Badri Roysam ; Nicholas Heller ; Pedro Henriques Abreu ; Jose Pereira Amorim ; Ivana Isgum ; Vishal Patel ; Kevin Zhou ; Steve Jiang ; Ngan Le ; Khoa Luu ; Raphael Sznitman ; Veronika Cheplygina ; Samaneh Abbasi ; Diana Mateus ; Emanuele Trucco. Springer Science and Business Media Deutschland GmbH, 2020. pp. 233-241 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Medical reports are an essential medium in recording a patient{\textquoteright}s condition throughout a clinical trial. They contain valuable information that can be extracted to generate a large labeled dataset needed for the development of clinical tools. However, the majority of medical reports are stored in an unregularized format, and a trained human annotator (typically a doctor) must manually assess and label each case, resulting in an expensive and time consuming procedure. In this work, we present a framework for developing a multilingual breast MRI report classifier using a custom-built language representation called LAMBR. Our proposed method overcomes practical challenges faced in clinical settings, and we demonstrate improved performance in extracting labels from medical reports when compared with conventional approaches.",

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editor = "Jaime Cardoso and Wilson Silva and Ricardo Cruz and {Van Nguyen}, Hien and Badri Roysam and Nicholas Heller and {Henriques Abreu}, Pedro and {Pereira Amorim}, Jose and Ivana Isgum and Vishal Patel and Kevin Zhou and Steve Jiang and Ngan Le and Khoa Luu and Raphael Sznitman and Veronika Cheplygina and Samaneh Abbasi and Diana Mateus and Emanuele Trucco",

booktitle = "Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings",

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Tsai, CH, Kiryati, N, Konen, E, Sklair-Levy, M & Mayer, A 2020, Labeling of Multilingual Breast MRI Reports. in J Cardoso, W Silva, R Cruz, H Van Nguyen, B Roysam, N Heller, P Henriques Abreu, J Pereira Amorim, I Isgum, V Patel, K Zhou, S Jiang, N Le, K Luu, R Sznitman, V Cheplygina, S Abbasi, D Mateus & E Trucco (eds), Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12446 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 233-241, 3rd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the 2nd International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, Peru, 4/10/20. https://doi.org/10.1007/978-3-030-61166-8_25

Labeling of Multilingual Breast MRI Reports. / Tsai, Chen Han; Kiryati, Nahum; Konen, Eli et al.

Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Jaime Cardoso; Wilson Silva; Ricardo Cruz; Hien Van Nguyen; Badri Roysam; Nicholas Heller; Pedro Henriques Abreu; Jose Pereira Amorim; Ivana Isgum; Vishal Patel; Kevin Zhou; Steve Jiang; Ngan Le; Khoa Luu; Raphael Sznitman; Veronika Cheplygina; Samaneh Abbasi; Diana Mateus; Emanuele Trucco. Springer Science and Business Media Deutschland GmbH, 2020. p. 233-241 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12446 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sklair-Levy, Miri

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A2 - Van Nguyen, Hien

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A2 - Pereira Amorim, Jose

A2 - Isgum, Ivana

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A2 - Zhou, Kevin

A2 - Jiang, Steve

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A2 - Luu, Khoa

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Tsai CH, Kiryati N, Konen E, Sklair-Levy M, Mayer A. Labeling of Multilingual Breast MRI Reports. In Cardoso J, Silva W, Cruz R, Van Nguyen H, Roysam B, Heller N, Henriques Abreu P, Pereira Amorim J, Isgum I, Patel V, Zhou K, Jiang S, Le N, Luu K, Sznitman R, Cheplygina V, Abbasi S, Mateus D, Trucco E, editors, Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 233-241. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-61166-8_25

Labeling of Multilingual Breast MRI Reports

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Keywords

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