D9 - Efficient Video-Based Deep Learning for Ultrasound Guided Needle Insertion

Jonathan Rubin, Alvin Chen, Anumod Odungattu Thodiyil, Raghavendra Srinivasa Naidu, Ramon Erkamp, Jon Fincke, Balasundar Raju

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We investigate video-based deep learning approaches for detecting needle insertions in ultrasound videos. We introduce two efficient and conceptually simple extensions to convert standard 2D object detectors into video object detectors that make use of temporal information from a history of frames. We compare our approaches to a 2D baseline method that makes independent predictions per frame. Given the need to run in real-time on computationally restricted environments, emphasis is placed on low computational complexity.
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Wednesday 7th July
D4-12 (short): Detection and Diagnosis 1 - 16:45 - 17:30 (UTC+2)
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