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Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with
  Low GPU Memory Requirements

Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements

26 November 2021
Franz Thaler
Christian Payer
Horst Bischof
Darko Štern
ArXivPDFHTML

Papers citing "Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements"

5 / 5 papers shown
Title
Knowledge Distillation from Cross Teaching Teachers for Efficient
  Semi-Supervised Abdominal Organ Segmentation in CT
Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT
Jae Won Choi
29
4
0
11 Nov 2022
Large Batch and Patch Size Training for Medical Image Segmentation
Large Batch and Patch Size Training for Medical Image Segmentation
Junya Sato
Shoji Kido
19
2
0
24 Oct 2022
Combining Self-Training and Hybrid Architecture for Semi-supervised
  Abdominal Organ Segmentation
Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation
Wentao Liu
Weijing Xu
Songlin Yan
Lemeng Wang
Haoyuan Li
Huihua Yang
17
7
0
23 Jul 2022
Deep learning for cardiac image segmentation: A review
Deep learning for cardiac image segmentation: A review
C. L. P. Chen
C. Qin
Huaqi Qiu
G. Tarroni
Jinming Duan
Wenjia Bai
Daniel Rueckert
SSeg
3DV
64
674
0
09 Nov 2019
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
266
7,636
0
03 Jul 2012
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