212 related articles for article (PubMed ID: 34254225)
1. 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]
2. 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]
3. CTG-Net: Cross-task guided network for breast ultrasound diagnosis.
Yang K; Suzuki A; Ye J; Nosato H; Izumori A; Sakanashi H
PLoS One; 2022; 17(8):e0271106. PubMed ID: 35951606
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. Multi-task learning for segmentation and classification of breast tumors from ultrasound images.
He Q; Yang Q; Su H; Wang Y
Comput Biol Med; 2024 May; 173():108319. PubMed ID: 38513394
[TBL] [Abstract][Full Text] [Related]
7. An attention-supervised full-resolution residual network for the segmentation of breast ultrasound images.
Qu X; Shi Y; Hou Y; Jiang J
Med Phys; 2020 Nov; 47(11):5702-5714. PubMed ID: 32964449
[TBL] [Abstract][Full Text] [Related]
8. BUS-Net: Breast Tumour Detection Network for Ultrasound Images Using Bi-directional ConvLSTM and Dense Residual Connections.
Arora R; Raman B
J Digit Imaging; 2023 Apr; 36(2):627-646. PubMed ID: 36515746
[TBL] [Abstract][Full Text] [Related]
9. BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis.
Zhang E; Seiler S; Chen M; Lu W; Gu X
Phys Med Biol; 2020 Jun; 65(12):125005. PubMed ID: 32155605
[TBL] [Abstract][Full Text] [Related]
10. Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images.
Zhou Y; Chen H; Li Y; Liu Q; Xu X; Wang S; Yap PT; Shen D
Med Image Anal; 2021 May; 70():101918. PubMed ID: 33676100
[TBL] [Abstract][Full Text] [Related]
11. Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework.
Fan Z; Gong P; Tang S; Lee CU; Zhang X; Song P; Chen S; Li H
Med Image Anal; 2023 Dec; 90():102960. PubMed ID: 37769552
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images.
Tasnim J; Hasan MK
Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38056017
[No Abstract] [Full Text] [Related]
14. Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images.
Ferreira MR; Torres HR; Oliveira B; Gomes-Fonseca J; Morais P; Novais P; Vilaca JL
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():3878-3881. PubMed ID: 36085645
[TBL] [Abstract][Full Text] [Related]
15. A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.
Chowdary J; Yogarajah P; Chaurasia P; Guruviah V
Ultrason Imaging; 2022 Jan; 44(1):3-12. PubMed ID: 35128997
[TBL] [Abstract][Full Text] [Related]
16. A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images.
Qu X; Lu H; Tang W; Wang S; Zheng D; Hou Y; Jiang J
Med Phys; 2022 Sep; 49(9):5787-5798. PubMed ID: 35866492
[TBL] [Abstract][Full Text] [Related]
17. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN.
Lei Y; He X; Yao J; Wang T; Wang L; Li W; Curran WJ; Liu T; Xu D; Yang X
Med Phys; 2021 Jan; 48(1):204-214. PubMed ID: 33128230
[TBL] [Abstract][Full Text] [Related]
18. A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound.
Gómez-Flores W; Coelho de Albuquerque Pereira W
Comput Biol Med; 2020 Nov; 126():104036. PubMed ID: 33059238
[TBL] [Abstract][Full Text] [Related]
19. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures.
Cao Z; Duan L; Yang G; Yue T; Chen Q
BMC Med Imaging; 2019 Jul; 19(1):51. PubMed ID: 31262255
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
