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The Trauma THOMPSON Challenge Report MICCAI 2023

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AI for Brain Lesion Detection and Trauma Video Action Recognition (TTC 2023, BONBID-HIE 2023)

Abstract

Trauma TeleHelper for Operational Medical Procedure Support and Offline Network (Trauma THOMPSON) Challenge offers a first egocentric view dataset on emergency care procedures under resource constrained scenarios. This paper describes the baseline solutions to the four tasks of the Trauma THOMPSON Challenge 2023. They were not provided to the participants ahead of the challenge and was not part of the competition. The Temporal Segmentation Network algorithm is used for the action recognition task with top-1 accuracy of 11.38%. It is also used for the action anticipation task with top-1 accuracy of 5.82% and procedure recognition task with top-1 accuracy of 53.57%. ViLT is adopted for the Visual Question Answering challenge with an aggregate accuracy of 39.13%.

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Correspondence to Juan Wachs .

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Disclaimers: The views expressed are those of the author(s) and do not reflect the official policy of the Department of the Army, the Department of Defense, or the U.S. Government. The investigators have adhered to the policies for the protection of human subjects as prescribed in 45 CFR 46.

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Zhuo, Y., W. Kirkpatrick, A., Couperus, K., Tran, O., Wachs, J. (2025). The Trauma THOMPSON Challenge Report MICCAI 2023. In: Bao, R., Grant, E., Kirkpatrick, A., Wachs, J., Ou, Y. (eds) AI for Brain Lesion Detection and Trauma Video Action Recognition. TTC BONBID-HIE 2023 2023. Lecture Notes in Computer Science, vol 14567. Springer, Cham. https://doi.org/10.1007/978-3-031-71626-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-71626-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71625-6

  • Online ISBN: 978-3-031-71626-3

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