TY - GEN
T1 - Overview of the Trauma THOMPSON Challenge at MICCAI 2023
AU - Zhuo, Yupeng
AU - Kirkpatrick, Andrew W.
AU - Couperus, Kyle
AU - Tran, Oanh
AU - Beck, Jonah
AU - DeVane, De Anna
AU - Candelore, Ross
AU - McKee, Jessica
AU - Colombo, Christopher
AU - Gorbatkin, Chad
AU - Birch, Eleanor
AU - Duerstock, Bradley
AU - Wachs, Juan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - This paper introduces the initial edition of the Trauma TeleHelper for Operational Medical Procedure Support and Offline Network (Trauma THOMPSON) Challenge. It was organized as a satellite event of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023. The challenge contains two tracks and four tasks related to automatic analysis of videos and images about emergency care procedures under resource constrained environments. The three tasks for Track 1, are (1) action recognition; (2) action anticipation; and (3) activity recognition. For Track 2, the only task was visual question answering. The videos were recorded by a team of doctors from the first-person view and annotated by medical professionals. The data were split into 70% for training and 30% for testing. For Task 1, the best method using VideoSwin with Swin-S and ThreeCrop achieved a Top 1 accuracy of 35.27%. For Task 2, the best method using VideoSwin with Swin-S and CenterCrop achieved Top 1 accuracy of 23.67%. No submission was received for Task 3. For the VQA task, the best method relying on MCAN-large with VinVL and FQCA obtained an accuracy of 74.35%.
AB - This paper introduces the initial edition of the Trauma TeleHelper for Operational Medical Procedure Support and Offline Network (Trauma THOMPSON) Challenge. It was organized as a satellite event of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023. The challenge contains two tracks and four tasks related to automatic analysis of videos and images about emergency care procedures under resource constrained environments. The three tasks for Track 1, are (1) action recognition; (2) action anticipation; and (3) activity recognition. For Track 2, the only task was visual question answering. The videos were recorded by a team of doctors from the first-person view and annotated by medical professionals. The data were split into 70% for training and 30% for testing. For Task 1, the best method using VideoSwin with Swin-S and ThreeCrop achieved a Top 1 accuracy of 35.27%. For Task 2, the best method using VideoSwin with Swin-S and CenterCrop achieved Top 1 accuracy of 23.67%. No submission was received for Task 3. For the VQA task, the best method relying on MCAN-large with VinVL and FQCA obtained an accuracy of 74.35%.
KW - Action Anticipation
KW - Action Recognition
KW - Procedure Recognition
KW - Visual Question Answering
UR - http://www.scopus.com/inward/record.url?scp=85208440890&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-71626-3_7
DO - 10.1007/978-3-031-71626-3_7
M3 - Conference contribution
AN - SCOPUS:85208440890
SN - 9783031716256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 60
BT - AI for Brain Lesion Detection and Trauma Video Action Recognition - 1st BONBID-HIE Lesion Segmentation Challenge and 1st Trauma Thompson Challenge, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Bao, Rina
A2 - Grant, Ellen
A2 - Ou, Yangming
A2 - Kirkpatrick, Andrew
A2 - Wachs, Juan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st BONBID-HIE Lesion Segmentation Challenge and 1st Trauma Thompson Challenge Held in Conjunction with 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023
Y2 - 12 October 2023 through 16 October 2023
ER -