TY - GEN
T1 - Biomedical concept detection in medical images
T2 - 20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019
AU - Singh, Sonit
AU - Karimi, Sarvnaz
AU - Ho-Shon, Kevin
AU - Hamey, Len
N1 - Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2019/9/9
Y1 - 2019/9/9
N2 - We describe our concept detection system submitted for the ImageCLEFmed Caption task, part of the ImageCLEF 2019 challenge. The advancements in imaging technologies has improved the ability of clinicians to detect, diagnose, and treat diseases. Radiologists routinely interpret medical images and summarise their findings in the form of radiology reports. The mapping of visual information present in medical images to the condensed textual description is a tedious, time-consuming, expensive, and error-prone task. The development of methods that can automatically detect the presence and location of medical concepts in medical images can improve the efficiency of radiologists, reduce the burden of manual interpretation, and also help in reducing diagnostic errors. We propose a Convolutional Neural Network based multi-label image classifier to predict relevant concepts present in medical images. The proposed method achieved an F1-score of 0.1435 on the held-out test set of the 2019 ImageCLEFmed Caption Task. We present our proposed system with data analysis, experimental results, comparison, and discussion.
AB - We describe our concept detection system submitted for the ImageCLEFmed Caption task, part of the ImageCLEF 2019 challenge. The advancements in imaging technologies has improved the ability of clinicians to detect, diagnose, and treat diseases. Radiologists routinely interpret medical images and summarise their findings in the form of radiology reports. The mapping of visual information present in medical images to the condensed textual description is a tedious, time-consuming, expensive, and error-prone task. The development of methods that can automatically detect the presence and location of medical concepts in medical images can improve the efficiency of radiologists, reduce the burden of manual interpretation, and also help in reducing diagnostic errors. We propose a Convolutional Neural Network based multi-label image classifier to predict relevant concepts present in medical images. The proposed method achieved an F1-score of 0.1435 on the held-out test set of the 2019 ImageCLEFmed Caption Task. We present our proposed system with data analysis, experimental results, comparison, and discussion.
KW - Caption Prediction
KW - Computer Vision
KW - Concept Detection
KW - Convolutional Neural Network
KW - Medical Imaging
KW - Multi-label classification
UR - http://www.scopus.com/inward/record.url?scp=85070523349&partnerID=8YFLogxK
UR - http://ceur-ws.org/Vol-2380/
M3 - Conference proceeding contribution
AN - SCOPUS:85070523349
T3 - CEUR Workshop Proceedings
SP - 1
EP - 15
BT - CLEF 2019 Working Notes
A2 - Cappellato, Linda
A2 - Ferro, Nicola
A2 - Losada, David E.
A2 - Müller, Henning
PB - CEUR
CY - Aachen, Germany
Y2 - 9 September 2019 through 12 September 2019
ER -