2021
DOI: 10.48550/arxiv.2110.15884
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Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Abstract: Most research on novel techniques for 3D Medical Image Segmentation (MIS) is currently done using Deep Learning with GPU accelerators. The principal challenge of such technique is that a single input can easily cope computing resources, and require prohibitive amounts of time to be processed. Distribution of deep learning and scalability over computing devices is an actual need for progressing on such research field. Conventional distribution of neural networks consist in "data parallelism", where data is scat… Show more

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“…TUNet performance on different applications relies significantly on the various hyperparameters that govern the network architecture (Kinnison et al, 2018;Li et al, 2021;Berral et al, 2021). As such, the DLSIA API offers full flexibility in creating and deploying TUNets of custom sizes and morphology by allowing the user to define the four following architecture-governing hyperparameters:…”
Section: Tunable U-netsmentioning
confidence: 99%
“…TUNet performance on different applications relies significantly on the various hyperparameters that govern the network architecture (Kinnison et al, 2018;Li et al, 2021;Berral et al, 2021). As such, the DLSIA API offers full flexibility in creating and deploying TUNets of custom sizes and morphology by allowing the user to define the four following architecture-governing hyperparameters:…”
Section: Tunable U-netsmentioning
confidence: 99%