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Self and Mixed Supervision to Improve Training Labels for Multi-Class
  Medical Image Segmentation

Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation

6 March 2024
Jianfei Liu
Christopher Parnell
Ronald M. Summers
ArXivPDFHTML

Papers citing "Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation"

5 / 5 papers shown
Title
TotalSegmentator: robust segmentation of 104 anatomical structures in CT
  images
TotalSegmentator: robust segmentation of 104 anatomical structures in CT images
Jakob Wasserthal
H. Breit
Manfred T. Meyer
M. Pradella
Daniel Hinck
...
Daniel Boll
Joshy Cyriac
Shan Yang
M. Bach
Martin Segeroth
OOD
30
708
0
11 Aug 2022
Learning with Limited Annotations: A Survey on Deep Semi-Supervised
  Learning for Medical Image Segmentation
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Rushi Jiao
Yichi Zhang
Leiting Ding
Rong Cai
Jicong Zhang
34
154
0
28 Jul 2022
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive
  Pseudo Labeling and Informative Active Annotation
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation
Wenqiao Zhang
Lei Zhu
James Hallinan
A. Makmur
Shengyu Zhang
Qingpeng Cai
Beng Chin Ooi
67
82
0
04 Mar 2022
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
Yassine Ouali
C´eline Hudelot
Myriam Tami
96
720
0
19 Mar 2020
Generalised Dice overlap as a deep learning loss function for highly
  unbalanced segmentations
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Carole H Sudre
Wenqi Li
Tom Vercauteren
Sébastien Ourselin
M. Jorge Cardoso
SSeg
79
2,132
0
11 Jul 2017
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