298 related articles for article (PubMed ID: 35672595)
1. Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net.
Wu Y; Shen H; Tan Y; Shi Y
Int J Comput Assist Radiol Surg; 2022 Oct; 17(10):1915-1922. PubMed ID: 35672595
[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. A multiple-channel and atrous convolution network for ultrasound image segmentation.
Zhang L; Zhang J; Li Z; Song Y
Med Phys; 2020 Dec; 47(12):6270-6285. PubMed ID: 33007105
[TBL] [Abstract][Full Text] [Related]
4. SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography.
Wang J; Lv P; Wang H; Shi C
Comput Methods Programs Biomed; 2021 Sep; 208():106268. PubMed ID: 34274611
[TBL] [Abstract][Full Text] [Related]
5. Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images.
Seo H; Huang C; Bassenne M; Xiao R; Xing L
IEEE Trans Med Imaging; 2020 May; 39(5):1316-1325. PubMed ID: 31634827
[TBL] [Abstract][Full Text] [Related]
6. ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.
Ji Z; Mu J; Liu J; Zhang H; Dai C; Zhang X; Ganchev I
Med Biol Eng Comput; 2024 Jun; 62(6):1673-1687. PubMed ID: 38326677
[TBL] [Abstract][Full Text] [Related]
7. SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.
Yang S; Liang Y; Wu S; Sun P; Chen Z
J Xray Sci Technol; 2024; 32(3):707-723. PubMed ID: 38552134
[TBL] [Abstract][Full Text] [Related]
8. CAM-Wnet: An effective solution for accurate pulmonary embolism segmentation.
Liu Z; Yuan H; Wang H
Med Phys; 2022 Aug; 49(8):5294-5303. PubMed ID: 35609213
[TBL] [Abstract][Full Text] [Related]
9. Hybrid-attention densely connected U-Net with GAP for extracting livers from CT volumes.
Chen Y; Hu F; Wang Y; Zheng C
Med Phys; 2022 Feb; 49(2):1015-1033. PubMed ID: 35015305
[TBL] [Abstract][Full Text] [Related]
10. RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation.
Li L; Ma H
Sensors (Basel); 2022 Mar; 22(7):. PubMed ID: 35408067
[TBL] [Abstract][Full Text] [Related]
11. Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images.
Liu T; Liu J; Ma Y; He J; Han J; Ding X; Chen CT
Med Phys; 2021 Jan; 48(1):264-272. PubMed ID: 33159809
[TBL] [Abstract][Full Text] [Related]
12. RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images.
Jiang L; Ou J; Liu R; Zou Y; Xie T; Xiao H; Bai T
Comput Biol Med; 2023 May; 158():106838. PubMed ID: 37030263
[TBL] [Abstract][Full Text] [Related]
13. Multi-scale contextual semantic enhancement network for 3D medical image segmentation.
Xia T; Huang G; Pun CM; Zhang W; Li J; Ling WK; Lin C; Yang Q
Phys Med Biol; 2022 Nov; 67(22):. PubMed ID: 36317277
[No Abstract] [Full Text] [Related]
14. 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]
15. 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]
16. A novel MCF-Net: Multi-level context fusion network for 2D medical image segmentation.
Liu L; Liu Y; Zhou J; Guo C; Duan H
Comput Methods Programs Biomed; 2022 Nov; 226():107160. PubMed ID: 36191351
[TBL] [Abstract][Full Text] [Related]
17. ADR-Net: Context extraction network based on M-Net for medical image segmentation.
Ji L; Jiang X; Gao Y; Fang Z; Cai Q; Wei Z
Med Phys; 2020 Sep; 47(9):4254-4264. PubMed ID: 32602963
[TBL] [Abstract][Full Text] [Related]
18. [Lung parenchyma segmentation based on double scale parallel attention network].
Feng K; Ren L; Wu Y; Li Y; Wang H; Wang G
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2022 Aug; 39(4):721-729. PubMed ID: 36008336
[TBL] [Abstract][Full Text] [Related]
19. Tumor attention networks: Better feature selection, better tumor segmentation.
Pang S; Du A; Orgun MA; Wang Y; Yu Z
Neural Netw; 2021 Aug; 140():203-222. PubMed ID: 33780873
[TBL] [Abstract][Full Text] [Related]
20. DCU-Net: Multi-scale U-Net for brain tumor segmentation.
Yang T; Zhou Y; Li L; Zhu C
J Xray Sci Technol; 2020; 28(4):709-726. PubMed ID: 32444591
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
