Fetching the paper…
Reading the bibliography…
The Segment Anything Model (SAM) has recently emerged as a significant breakthrough in foundation models, demonstrating remarkable zero-shot performance in object segmentation tasks.
“Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?”
Olivier Bernard, Alain Lalande and Clement Zotti · 2018
Earlier work this paper cites.
“Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: the M&Ms Challenge”
Victor Campello, Polyxeni Gkontra and Cristian Izquierdo · 2021
Earlier work this paper cites.
“nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation”
Fabian Isensee et al · 2021
Earlier work this paper cites.
“Segment Anything” arXiv:2304.02643 [cs]
Alexander Kirillov et al · 2023
Cited alongside, same era.
“Segment Anything Model for Medical Images?” arXiv:2304.14660 [cs, eess]
Yuhao Huang et al · 2023
Cited alongside, same era.
“SAM on Medical Images: A Comprehensive Study on Three Prompt Modes” arXiv:2305.00035 [cs]
Dongjie Cheng et al · 2023
Cited alongside, same era.
“Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets”
Sheng He, Rina Bao and Jingpeng Li · 2023
Closest in time.
“Segment Anything in Medical Images” arXiv:2304.12306 [cs, eess]
Jun Ma et al · 2023
Closest in time.
“Sparse annotation strategies for segmentation of short axis cardiac MRI”, 2023
Josh Stein, Maxime Di Folco and Julia Schnabel · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…