281 related articles for article (PubMed ID: 33684731)
1. MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images.
Kushnure DT; Talbar SN
Comput Med Imaging Graph; 2021 Apr; 89():101885. PubMed ID: 33684731
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.
Zhang D; Yang Z; Jiang S; Zhou Z; Meng M; Wang W
J Appl Clin Med Phys; 2020 Oct; 21(10):158-169. PubMed ID: 32991783
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. A Stacked Generalization U-shape network based on zoom strategy and its application in biomedical image segmentation.
Shi T; Jiang H; Zheng B
Comput Methods Programs Biomed; 2020 Dec; 197():105678. PubMed ID: 32791449
[TBL] [Abstract][Full Text] [Related]
6. MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation.
Xi H; Dong H; Sheng Y; Cui H; Huang C; Li J; Zhu J
Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38061069
[No Abstract] [Full Text] [Related]
7. Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images.
Zhang B; Qi S; Wu Y; Pan X; Yao Y; Qian W; Guan Y
Comput Methods Programs Biomed; 2022 Jul; 222():106946. PubMed ID: 35716533
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation.
Chen Y; Wang K; Liao X; Qian Y; Wang Q; Yuan Z; Heng PA
Front Genet; 2019; 10():1110. PubMed ID: 31827487
[TBL] [Abstract][Full Text] [Related]
10. Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT.
Wang J; Zhang X; Guo L; Shi C; Tamura S
Math Biosci Eng; 2023 Jan; 20(1):1297-1316. PubMed ID: 36650812
[TBL] [Abstract][Full Text] [Related]
11. MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging.
Hettihewa K; Kobchaisawat T; Tanpowpong N; Chalidabhongse TH
Sci Rep; 2023 Nov; 13(1):20098. PubMed ID: 37973987
[TBL] [Abstract][Full Text] [Related]
12. U-Net combined with multi-scale attention mechanism for liver segmentation in CT images.
Wu J; Zhou S; Zuo S; Chen Y; Sun W; Luo J; Duan J; Wang H; Wang D
BMC Med Inform Decis Mak; 2021 Oct; 21(1):283. PubMed ID: 34654419
[TBL] [Abstract][Full Text] [Related]
13. MS-FANet: Multi-scale feature attention network for liver tumor segmentation.
Chen Y; Zheng C; Zhang W; Lin H; Chen W; Zhang G; Xu G; Wu F
Comput Biol Med; 2023 Sep; 163():107208. PubMed ID: 37421737
[TBL] [Abstract][Full Text] [Related]
14. Liver tumor segmentation based on 3D convolutional neural network with dual scale.
Meng L; Tian Y; Bu S
J Appl Clin Med Phys; 2020 Jan; 21(1):144-157. PubMed ID: 31793212
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.
Hille G; Agrawal S; Tummala P; Wybranski C; Pech M; Surov A; Saalfeld S
Comput Methods Programs Biomed; 2023 Oct; 240():107647. PubMed ID: 37329803
[TBL] [Abstract][Full Text] [Related]
17. Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+.
Li J; Liu K; Hu Y; Zhang H; Heidari AA; Chen H; Zhang W; Algarni AD; Elmannai H
Comput Biol Med; 2023 May; 158():106501. PubMed ID: 36635120
[TBL] [Abstract][Full Text] [Related]
18. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
[TBL] [Abstract][Full Text] [Related]
19. Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.
Özcan F; Uçan ON; Karaçam S; Tunçman D
Bioengineering (Basel); 2023 Feb; 10(2):. PubMed ID: 36829709
[TBL] [Abstract][Full Text] [Related]
20. PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images.
Xu Y; Cai M; Lin L; Zhang Y; Hu H; Peng Z; Zhang Q; Chen Q; Mao X; Iwamoto Y; Han XH; Chen YW; Tong R
Med Phys; 2021 Jul; 48(7):3752-3766. PubMed ID: 33950526
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]