Titlebook: AI for Brain Lesion Detection and Trauma Video Action Recognition; First BONBID-HIE Les Rina Bao,Ellen Grant,Yangming Ou Conference proceed

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叢林

Objekt, Ereignis, Ereignisprozedur,ate prediction of both the verb and noun components of the action, given that actions consist of both a verb and a noun. In the end, we selected the predictions generated by Video-Swin as our final submission, achieving a Top-1 Action accuracy of . for Action Recognition and Top-1 Action accuracy of

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Overview of?the?Trauma THOMPSON Challenge at?MICCAI 2023chieved 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%.

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Action Recognition and?Action Anticipation Tasks in?the?Trauma THOMPSON Challenge Technical Reportate prediction of both the verb and noun components of the action, given that actions consist of both a verb and a noun. In the end, we selected the predictions generated by Video-Swin as our final submission, achieving a Top-1 Action accuracy of . for Action Recognition and Top-1 Action accuracy of

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,Nichtnumerische Speicherpl?tze,rucial for diagnosis and treatment. However, traditional deep learning models often struggle with HIE’s diverse lesion characteristics. This paper presents a novel ensemble strategy utilizing Swin-UNETR, a transformer-based model, to address this challenge. We demonstrate the advantages of Swin-UNET

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