Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Gaillochet, Mélanie"'
Foundation models such as the recently introduced Segment Anything Model (SAM) have achieved remarkable results in image segmentation tasks. However, these models typically require user interaction through handcrafted prompts such as bounding boxes,
Externí odkaz:
http://arxiv.org/abs/2409.20293
The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and inte
Externí odkaz:
http://arxiv.org/abs/2301.07670
Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing which samples are best to annotate in order to maximize the performance
Externí odkaz:
http://arxiv.org/abs/2301.06624
Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Recently, many deep learning techniques have been developed, addressing this issue of recovering the fully sampled MR image from
Externí odkaz:
http://arxiv.org/abs/2007.13123
Autor:
Gaillochet M; ETS Montréal, 1100 Notre-Dame St W, Montreal H3C 1K3, QC, Canada. Electronic address: melanie.gaillochet.1@ens.etsmtl.ca., Desrosiers C; ETS Montréal, 1100 Notre-Dame St W, Montreal H3C 1K3, QC, Canada., Lombaert H; ETS Montréal, 1100 Notre-Dame St W, Montreal H3C 1K3, QC, Canada.
Publikováno v:
Medical image analysis [Med Image Anal] 2023 Dec; Vol. 90, pp. 102958. Date of Electronic Publication: 2023 Sep 12.
