667 related articles for article (PubMed ID: 33735451)
1. Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion.
Yuan C; Zhang M; Huang X; Xie W; Lin X; Zhao W; Li B; Qian D
Med Phys; 2021 Jul; 48(7):3665-3678. PubMed ID: 33735451
[TBL] [Abstract][Full Text] [Related]
2. Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images.
Huang Z; Guo Y; Zhang N; Huang X; Decazes P; Becker S; Ruan S
Comput Biol Med; 2022 Dec; 151(Pt A):106230. PubMed ID: 36306574
[TBL] [Abstract][Full Text] [Related]
3. A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.
Yue Y; Li N; Zhang G; Xing W; Zhu Z; Liu X; Song S; Ta D
Comput Methods Programs Biomed; 2024 Jun; 251():108216. PubMed ID: 38761412
[TBL] [Abstract][Full Text] [Related]
4. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
Blanc-Durand P; Jégou S; Kanoun S; Berriolo-Riedinger A; Bodet-Milin C; Kraeber-Bodéré F; Carlier T; Le Gouill S; Casasnovas RO; Meignan M; Itti E
Eur J Nucl Med Mol Imaging; 2021 May; 48(5):1362-1370. PubMed ID: 33097974
[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. Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer.
Kumar A; Fulham M; Feng D; Kim J
IEEE Trans Med Imaging; 2019 Jun; ():. PubMed ID: 31217099
[TBL] [Abstract][Full Text] [Related]
7. Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation.
Bi L; Fulham M; Li N; Liu Q; Song S; Dagan Feng D; Kim J
Comput Methods Programs Biomed; 2021 May; 203():106043. PubMed ID: 33744750
[TBL] [Abstract][Full Text] [Related]
8. Deep learning-based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images.
Jiang C; Chen K; Teng Y; Ding C; Zhou Z; Gao Y; Wu J; He J; He K; Zhang J
Eur Radiol; 2022 Jul; 32(7):4801-4812. PubMed ID: 35166895
[TBL] [Abstract][Full Text] [Related]
9. Multimodal deep learning model on interim [
Yuan C; Shi Q; Huang X; Wang L; He Y; Li B; Zhao W; Qian D
Eur Radiol; 2023 Jan; 33(1):77-88. PubMed ID: 36029345
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss.
Xu Y; Klyuzhin I; Harsini S; Ortiz A; Zhang S; Bénard F; Dodhia R; Uribe CF; Rahmim A; Lavista Ferres J
Comput Biol Med; 2023 May; 158():106882. PubMed ID: 37037147
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 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]
16. Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy.
Shao M; Cheng C; Hu C; Zheng J; Zhang B; Wang T; Jin G; Liu Z; Zuo C
Quant Imaging Med Surg; 2024 Feb; 14(2):1747-1765. PubMed ID: 38415108
[TBL] [Abstract][Full Text] [Related]
17. HFCF-Net: A hybrid-feature cross fusion network for COVID-19 lesion segmentation from CT volumetric images.
Wang Y; Yang Q; Tian L; Zhou X; Rekik I; Huang H
Med Phys; 2022 Jun; 49(6):3797-3815. PubMed ID: 35301729
[TBL] [Abstract][Full Text] [Related]
18. Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.
Chen J; Li Y; Luna LP; Chung HW; Rowe SP; Du Y; Solnes LB; Frey EC
Med Phys; 2021 Jul; 48(7):3860-3877. PubMed ID: 33905560
[TBL] [Abstract][Full Text] [Related]
19. A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers.
Groendahl AR; Skjei Knudtsen I; Huynh BN; Mulstad M; Moe YM; Knuth F; Tomic O; Indahl UG; Torheim T; Dale E; Malinen E; Futsaether CM
Phys Med Biol; 2021 Mar; 66(6):065012. PubMed ID: 33666176
[TBL] [Abstract][Full Text] [Related]
20. HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images.
Kushnure DT; Talbar SN
Comput Methods Programs Biomed; 2022 Jan; 213():106501. PubMed ID: 34752959
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]