194 related articles for article (PubMed ID: 36156438)
1. ISA-Net: Improved spatial attention network for PET-CT tumor segmentation.
Huang Z; Zou S; Wang G; Chen Z; Shen H; Wang H; Zhang N; Zhang L; Yang F; Wang H; Liang D; Niu T; Zhu X; Hu Z
Comput Methods Programs Biomed; 2022 Nov; 226():107129. PubMed ID: 36156438
[TBL] [Abstract][Full Text] [Related]
2. SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET/CT images.
Li GY; Chen J; Jang SI; Gong K; Li Q
Med Phys; 2024 Mar; 51(3):2096-2107. PubMed ID: 37776263
[TBL] [Abstract][Full Text] [Related]
3. DMCT-Net: dual modules convolution transformer network for head and neck tumor segmentation in PET/CT.
Wang J; Peng Y; Guo Y
Phys Med Biol; 2023 May; 68(11):. PubMed ID: 37141902
[No Abstract] [Full Text] [Related]
4. MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention.
Wu Y; Jiang H; Pang W
Comput Biol Med; 2023 May; 158():106818. PubMed ID: 36966557
[TBL] [Abstract][Full Text] [Related]
5. Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.
Lei Y; Wang T; Jeong JJ; Janopaul-Naylor J; Kesarwala AH; Roper J; Tian S; Bradley JD; Liu T; Higgins K; Yang X
Med Phys; 2023 Jan; 50(1):274-283. PubMed ID: 36203393
[TBL] [Abstract][Full Text] [Related]
6. LSAM: L2-norm self-attention and latent space feature interaction for automatic 3D multi-modal head and neck tumor segmentation.
Li L; Tan J; Yu L; Li C; Nan H; Zheng S
Phys Med Biol; 2023 Nov; 68(22):. PubMed ID: 37852283
[No Abstract] [Full Text] [Related]
7. Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network.
Guo Z; Guo N; Gong K; Zhong S; Li Q
Phys Med Biol; 2019 Oct; 64(20):205015. PubMed ID: 31514173
[TBL] [Abstract][Full Text] [Related]
8. Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation.
Fu X; Bi L; Kumar A; Fulham M; Kim J
IEEE J Biomed Health Inform; 2021 Sep; 25(9):3507-3516. PubMed ID: 33591922
[TBL] [Abstract][Full Text] [Related]
9. MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images.
Wang F; Cheng C; Cao W; Wu Z; Wang H; Wei W; Yan Z; Liu Z
Comput Biol Med; 2023 Mar; 155():106657. PubMed ID: 36791551
[TBL] [Abstract][Full Text] [Related]
10. DGCBG-Net: A dual-branch network with global cross-modal interaction and boundary guidance for tumor segmentation in PET/CT images.
Zou Z; Zou B; Kui X; Chen Z; Li Y
Comput Methods Programs Biomed; 2024 Jun; 250():108125. PubMed ID: 38631130
[TBL] [Abstract][Full Text] [Related]
11. Information fusion for fully automated segmentation of head and neck tumors from PET and CT images.
Shiri I; Amini M; Yousefirizi F; Vafaei Sadr A; Hajianfar G; Salimi Y; Mansouri Z; Jenabi E; Maghsudi M; Mainta I; Becker M; Rahmim A; Zaidi H
Med Phys; 2024 Jan; 51(1):319-333. PubMed ID: 37475591
[TBL] [Abstract][Full Text] [Related]
12. MRLA-Net: A tumor segmentation network embedded with a multiple receptive-field lesion attention module in PET-CT images.
Zhou Y; Jiang H; Diao Z; Tong G; Luan Q; Li Y; Li X
Comput Biol Med; 2023 Feb; 153():106538. PubMed ID: 36646023
[TBL] [Abstract][Full Text] [Related]
13. Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.
Zhao LM; Zhang H; Kim DD; Ghimire K; Hu R; Kargilis DC; Tang L; Meng S; Chen Q; Liao WH; Bai H; Jiao Z; Feng X
Phys Med Biol; 2023 Apr; 68(9):. PubMed ID: 37019119
[No Abstract] [Full Text] [Related]
14. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.
Gou S; Tong N; Qi S; Yang S; Chin R; Sheng K
Phys Med Biol; 2020 Dec; 65(24):245034. PubMed ID: 32097892
[TBL] [Abstract][Full Text] [Related]
15. EFNet: evidence fusion network for tumor segmentation from PET-CT volumes.
Diao Z; Jiang H; Han XH; Yao YD; Shi T
Phys Med Biol; 2021 Oct; 66(20):. PubMed ID: 34555816
[TBL] [Abstract][Full Text] [Related]
16. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
17. Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images.
Wang Y; Lombardo E; Huang L; Avanzo M; Fanetti G; Franchin G; Zschaeck S; Weingärtner J; Belka C; Riboldi M; Kurz C; Landry G
Radiat Oncol; 2024 Jan; 19(1):3. PubMed ID: 38191431
[TBL] [Abstract][Full Text] [Related]
18. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.
Zhao X; Li L; Lu W; Tan S
Phys Med Biol; 2018 Dec; 64(1):015011. PubMed ID: 30523964
[TBL] [Abstract][Full Text] [Related]
19. Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA.
van Praagh GD; Nienhuis PH; Reijrink M; Davidse MEJ; Duff LM; Spottiswoode BS; Mulder DJ; Prakken NHJ; Scarsbrook AF; Morgan AW; Tsoumpas C; Wolterink JM; Mouridsen KB; Borra RJH; Sinha B; Slart RHJA
Med Phys; 2024 Jun; 51(6):4297-4310. PubMed ID: 38323867
[TBL] [Abstract][Full Text] [Related]
20. BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis.
Wu H; Peng L; Du D; Xu H; Lin G; Zhou Z; Lu L; Lv W
Phys Med Biol; 2024 Apr; 69(10):. PubMed ID: 38593831
[No Abstract] [Full Text] [Related]
[Next] [New Search]
