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Efficient, high-performance pancreatic segmentation using multi-scale feature extraction

2020-09-02 · Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus Makowski, Daniel Rueckert, Rickmer Braren

For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial. For example, existent segmentation algorithms based on natural images are neither efficient in their parameter use nor optimized for medical imaging. Here we present MoNet, a highly optimized neural-network-based pancreatic segmentation algorithm focused on achieving high performance by efficient multi-scale image feature utilization.

📄 PDF Abstract BibTeX arXiv:2009.00872

Code (1)

TUM-AIMED/MoNet 공식 구현 tf

Tasks

Segmentation Vocal Bursts Intensity Prediction

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