TY - GEN
T1 - Labeling of Multilingual Breast MRI Reports
AU - Tsai, Chen Han
AU - Kiryati, Nahum
AU - Konen, Eli
AU - Sklair-Levy, Miri
AU - Mayer, Arnaldo
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Breast MRI
KW - LAMBR
KW - Labeling
KW - Medical reports
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85092913981&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61166-8_25
DO - 10.1007/978-3-030-61166-8_25
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AN - SCOPUS:85092913981
SN - 9783030611651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 233
EP - 241
BT - 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
A2 - Cardoso, Jaime
A2 - Silva, Wilson
A2 - Cruz, Ricardo
A2 - Van Nguyen, Hien
A2 - Roysam, Badri
A2 - Heller, Nicholas
A2 - Henriques Abreu, Pedro
A2 - Pereira Amorim, Jose
A2 - Isgum, Ivana
A2 - Patel, Vishal
A2 - Zhou, Kevin
A2 - Jiang, Steve
A2 - Le, Ngan
A2 - Luu, Khoa
A2 - Sznitman, Raphael
A2 - Cheplygina, Veronika
A2 - Abbasi, Samaneh
A2 - Mateus, Diana
A2 - Trucco, Emanuele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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
Y2 - 4 October 2020 through 8 October 2020
ER -