209 related articles for article (PubMed ID: 22606669)
1. IMPST: A New Interactive Self-Training Approach to Segmentation Suspicious Lesions in Breast MRI.
Azmi R; Norozi N; Anbiaee R; Salehi L; Amirzadi A
J Med Signals Sens; 2011 May; 1(2):138-48. PubMed ID: 22606669
[TBL] [Abstract][Full Text] [Related]
2. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.
Azmi R; Pishgoo B; Norozi N; Yeganeh S
J Med Signals Sens; 2013 Apr; 3(2):94-106. PubMed ID: 24098863
[TBL] [Abstract][Full Text] [Related]
3. A Semi-Supervised Method for Tumor Segmentation in Mammogram Images.
Azary H; Abdoos M
J Med Signals Sens; 2020; 10(1):12-18. PubMed ID: 32166073
[TBL] [Abstract][Full Text] [Related]
4. Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.
Chaitanya K; Erdil E; Karani N; Konukoglu E
Med Image Anal; 2023 Jul; 87():102792. PubMed ID: 37054649
[TBL] [Abstract][Full Text] [Related]
5. A self-supervised strategy for fully automatic segmentation of renal dynamic contrast-enhanced magnetic resonance images.
Huang W; Li H; Wang R; Zhang X; Wang X; Zhang J
Med Phys; 2019 Oct; 46(10):4417-4430. PubMed ID: 31306492
[TBL] [Abstract][Full Text] [Related]
6. Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.
Xiao Z; Su Y; Deng Z; Zhang W
Comput Methods Programs Biomed; 2022 Nov; 226():107099. PubMed ID: 36116398
[TBL] [Abstract][Full Text] [Related]
7. Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.
Peng P; Wu D; Huang LJ; Wang J; Zhang L; Wu Y; Jiang Y; Lu Z; Lai KW; Xia K
Interdiscip Sci; 2024 Mar; 16(1):39-57. PubMed ID: 37486420
[TBL] [Abstract][Full Text] [Related]
8. MDT: semi-supervised medical image segmentation with mixup-decoupling training.
Long J; Ren Y; Yang C; Ren P; Zeng Z
Phys Med Biol; 2024 Mar; 69(6):. PubMed ID: 38324897
[No Abstract] [Full Text] [Related]
9. Semi Supervised Learning with Deep Embedded Clustering for Image Classification and Segmentation.
Enguehard J; O'Halloran P; Gholipour A
IEEE Access; 2019; 7():11093-11104. PubMed ID: 31588387
[TBL] [Abstract][Full Text] [Related]
10. MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation.
Chen G; Ru J; Zhou Y; Rekik I; Pan Z; Liu X; Lin Y; Lu B; Shi J
Neuroimage; 2021 Dec; 244():118568. PubMed ID: 34508895
[TBL] [Abstract][Full Text] [Related]
11. Review of MR image segmentation techniques using pattern recognition.
Bezdek JC; Hall LO; Clarke LP
Med Phys; 1993; 20(4):1033-48. PubMed ID: 8413011
[TBL] [Abstract][Full Text] [Related]
12. Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency.
Mittal S; Tatarchenko M; Brox T
IEEE Trans Pattern Anal Mach Intell; 2021 Apr; 43(4):1369-1379. PubMed ID: 31869780
[TBL] [Abstract][Full Text] [Related]
13. Semi-TMS: an efficient regularization-oriented triple-teacher semi-supervised medical image segmentation model.
Chen W; Zhou S; Liu X; Chen Y
Phys Med Biol; 2023 Oct; 68(20):. PubMed ID: 37699409
[No Abstract] [Full Text] [Related]
14. Self-supervised-RCNN for medical image segmentation with limited data annotation.
Felfeliyan B; Forkert ND; Hareendranathan A; Cornel D; Zhou Y; Kuntze G; Jaremko JL; Ronsky JL
Comput Med Imaging Graph; 2023 Oct; 109():102297. PubMed ID: 37729826
[TBL] [Abstract][Full Text] [Related]
15. Uncertainty-guided cross learning via CNN and transformer for semi-supervised honeycomb lung lesion segmentation.
Zi-An Z; Xiu-Fang F; Xiao-Qiang R; Yun-Yun D
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37988756
[No Abstract] [Full Text] [Related]
16. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.
Rachmadi MF; Valdés-Hernández MDC; Li H; Guerrero R; Meijboom R; Wiseman S; Waldman A; Zhang J; Rueckert D; Wardlaw J; Komura T
Comput Med Imaging Graph; 2020 Jan; 79():101685. PubMed ID: 31846826
[TBL] [Abstract][Full Text] [Related]
17. Fast and robust segmentation of white blood cell images by self-supervised learning.
Zheng X; Wang Y; Wang G; Liu J
Micron; 2018 Apr; 107():55-71. PubMed ID: 29425969
[TBL] [Abstract][Full Text] [Related]
18. Automated segmentation of lesions and organs at risk on [
Yazdani E; Karamzadeh-Ziarati N; Cheshmi SS; Sadeghi M; Geramifar P; Vosoughi H; Jahromi MK; Kheradpisheh SR
Cancer Imaging; 2024 Feb; 24(1):30. PubMed ID: 38424612
[TBL] [Abstract][Full Text] [Related]
19. Boundary-aware information maximization for self-supervised medical image segmentation.
Peng J; Wang P; Pedersoli M; Desrosiers C
Med Image Anal; 2024 May; 94():103150. PubMed ID: 38574545
[TBL] [Abstract][Full Text] [Related]
20. All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation.
Xu Z; Wang Y; Lu D; Yu L; Yan J; Luo J; Ma K; Zheng Y; Tong RK
IEEE J Biomed Health Inform; 2022 Jul; 26(7):3174-3184. PubMed ID: 35324450
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
