125 related articles for article (PubMed ID: 38447499)
1. An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss.
Manh V; Jia X; Xue W; Xu W; Mei Z; Dong Y; Zhou J; Huang R; Ni D
Comput Biol Med; 2024 Mar; 171():108137. PubMed ID: 38447499
[TBL] [Abstract][Full Text] [Related]
2. Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network.
Han L; Huang Y; Dou H; Wang S; Ahamad S; Luo H; Liu Q; Fan J; Zhang J
Comput Methods Programs Biomed; 2020 Jun; 189():105275. PubMed ID: 31978805
[TBL] [Abstract][Full Text] [Related]
3. Ultrasonic breast tumor extraction based on adversarial mechanism and active contour.
Wang J; Chen G; Chen S; Joseph Raj AN; Zhuang Z; Xie L; Ma S
Comput Methods Programs Biomed; 2022 Oct; 225():107052. PubMed ID: 35985149
[TBL] [Abstract][Full Text] [Related]
4. Endometrium segmentation on transvaginal ultrasound image using key-point discriminator.
Park H; Lee HJ; Kim HG; Ro YM; Shin D; Lee SR; Kim SH; Kong M
Med Phys; 2019 Sep; 46(9):3974-3984. PubMed ID: 31230366
[TBL] [Abstract][Full Text] [Related]
5. Global guidance network for breast lesion segmentation in ultrasound images.
Xue C; Zhu L; Fu H; Hu X; Li X; Zhang H; Heng PA
Med Image Anal; 2021 May; 70():101989. PubMed ID: 33640719
[TBL] [Abstract][Full Text] [Related]
6. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.
Ma J; Wu F; Jiang T; Zhao Q; Kong D
Int J Comput Assist Radiol Surg; 2017 Nov; 12(11):1895-1910. PubMed ID: 28762196
[TBL] [Abstract][Full Text] [Related]
7. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.
Hu Y; Guo Y; Wang Y; Yu J; Li J; Zhou S; Chang C
Med Phys; 2019 Jan; 46(1):215-228. PubMed ID: 30374980
[TBL] [Abstract][Full Text] [Related]
8. C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation.
Chen G; Dai Y; Zhang J
Comput Methods Programs Biomed; 2022 Oct; 225():107086. PubMed ID: 36044802
[TBL] [Abstract][Full Text] [Related]
9. Breast ultrasound image segmentation: A coarse-to-fine fusion convolutional neural network.
Wang K; Liang S; Zhong S; Feng Q; Ning Z; Zhang Y
Med Phys; 2021 Aug; 48(8):4262-4278. PubMed ID: 34053092
[TBL] [Abstract][Full Text] [Related]
10. Local and Context-Attention Adaptive LCA-Net for Thyroid Nodule Segmentation in Ultrasound Images.
Tao Z; Dang H; Shi Y; Wang W; Wang X; Ren S
Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015742
[TBL] [Abstract][Full Text] [Related]
11. Boundary-rendering network for breast lesion segmentation in ultrasound images.
Huang R; Lin M; Dou H; Lin Z; Ying Q; Jia X; Xu W; Mei Z; Yang X; Dong Y; Zhou J; Ni D
Med Image Anal; 2022 Aug; 80():102478. PubMed ID: 35691144
[TBL] [Abstract][Full Text] [Related]
12. SK-Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images.
Dai H; Xie W; Xia E
Med Phys; 2024 Mar; 51(3):1798-1811. PubMed ID: 37606374
[TBL] [Abstract][Full Text] [Related]
13. Boundary-Guided and Region-Aware Network With Global Scale-Adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images.
Hu K; Zhang X; Lee D; Xiong D; Zhang Y; Gao X
IEEE J Biomed Health Inform; 2023 Sep; 27(9):4421-4432. PubMed ID: 37310830
[TBL] [Abstract][Full Text] [Related]
14. BUS-Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.
Thomas C; Byra M; Marti R; Yap MH; Zwiggelaar R
Med Phys; 2023 May; 50(5):3223-3243. PubMed ID: 36794706
[TBL] [Abstract][Full Text] [Related]
15. Transfer learning for automatic joint segmentation of thyroid and breast lesions from ultrasound images.
Ma J; Bao L; Lou Q; Kong D
Int J Comput Assist Radiol Surg; 2022 Feb; 17(2):363-372. PubMed ID: 34881409
[TBL] [Abstract][Full Text] [Related]
16. ULS4US: universal lesion segmentation framework for 2D ultrasound images.
Wu X; Jiang Y; Xing H; Song W; Wu P; Cui XW; Xu G
Phys Med Biol; 2023 Aug; 68(16):. PubMed ID: 37343585
[No Abstract] [Full Text] [Related]
17. Lesion segmentation in breast ultrasound images using the optimized marked watershed method.
Shen X; Ma H; Liu R; Li H; He J; Wu X
Biomed Eng Online; 2021 Jun; 20(1):57. PubMed ID: 34098970
[TBL] [Abstract][Full Text] [Related]
18. SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification.
Zhang G; Zhao K; Hong Y; Qiu X; Zhang K; Wei B
Int J Comput Assist Radiol Surg; 2021 Oct; 16(10):1719-1725. PubMed ID: 34254225
[TBL] [Abstract][Full Text] [Related]
19. Fully automatic tumor segmentation of breast ultrasound images with deep learning.
Zhang S; Liao M; Wang J; Zhu Y; Zhang Y; Zhang J; Zheng R; Lv L; Zhu D; Chen H; Wang W
J Appl Clin Med Phys; 2023 Jan; 24(1):e13863. PubMed ID: 36495018
[TBL] [Abstract][Full Text] [Related]
20. SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image.
Ning Z; Zhong S; Feng Q; Chen W; Zhang Y
IEEE Trans Med Imaging; 2022 Feb; 41(2):476-490. PubMed ID: 34582349
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
