He Z - Search Results

1

Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis.

Li Y, He Z, Lu Y, Ma X, Guo Y, Xie Z, Qin G, Xu W, Xu Z, Chen W, Chen H. Li Y, et al. Among authors: he z. Phys Med Biol. 2021 Jan 30;66(3):035028. doi: 10.1088/1361-6560/ab98d0. Phys Med Biol. 2021. PMID: 32485700

2

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

Li X, Qin G, He Q, Sun L, Zeng H, He Z, Chen W, Zhen X, Zhou L. Li X, et al. Among authors: he z, he q. Eur Radiol. 2020 Feb;30(2):778-788. doi: 10.1007/s00330-019-06457-5. Epub 2019 Nov 5. Eur Radiol. 2020. PMID: 31691121

3

Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.

Li Y, He Z, Ma X, Zeng W, Liu J, Xu W, Xu Z, Wang S, Wen C, Zeng H, Wu J, Chen W, Lu Y. Li Y, et al. Among authors: he z. Med Phys. 2022 Jun;49(6):3749-3768. doi: 10.1002/mp.15631. Epub 2022 Apr 5. Med Phys. 2022. PMID: 35338787

4

Learning from adversarial medical images for X-ray breast mass segmentation.

Shen T, Gou C, Wang FY, He Z, Chen W. Shen T, et al. Among authors: he z. Comput Methods Programs Biomed. 2019 Oct;180:105012. doi: 10.1016/j.cmpb.2019.105012. Epub 2019 Aug 5. Comput Methods Programs Biomed. 2019. PMID: 31421601

5

Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs.

Jiang G, Wei J, Xu Y, He Z, Zeng H, Wu J, Qin G, Chen W, Lu Y. Jiang G, et al. Among authors: he z. IEEE Trans Med Imaging. 2021 Aug;40(8):2080-2091. doi: 10.1109/TMI.2021.3071544. Epub 2021 Jul 30. IEEE Trans Med Imaging. 2021. PMID: 33826513

6

Pure and Mixed Tubular Carcinoma of the Breast: Mammographic Features, Clinicopathological Characteristics and Prognostic Analysis.

Wen C, Xu W, Qin G, Zeng H, He Z, Wang S, Xu Z, Ma M, Luo Z, Chen W. Wen C, et al. Among authors: he z. Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211045198. doi: 10.1177/15330338211045198. Technol Cancer Res Treat. 2021. PMID: 34918991 Free PMC article.

7

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.

He Z, Li Y, Zeng W, Xu W, Liu J, Ma X, Wei J, Zeng H, Xu Z, Wang S, Wen C, Wu J, Feng C, Ma M, Qin G, Lu Y, Chen W. He Z, et al. Front Oncol. 2021 Dec 17;11:773389. doi: 10.3389/fonc.2021.773389. eCollection 2021. Front Oncol. 2021. PMID: 34976817 Free PMC article.

8

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

Jiang G, He Z, Zhou Y, Wei J, Xu Y, Zeng H, Wu J, Qin G, Chen W, Lu Y. Jiang G, et al. Among authors: he z. Med Phys. 2023 Feb;50(2):837-853. doi: 10.1002/mp.16007. Epub 2022 Oct 24. Med Phys. 2023. PMID: 36196045

9

The mammography and MRI manifestations of adenomyoepithelioma of the breast.

Zhang L, Qin G, He Z, Chen W, Yang L. Zhang L, et al. Among authors: he z. Clin Radiol. 2016 Mar;71(3):235-43. doi: 10.1016/j.crad.2015.11.005. Epub 2015 Dec 22. Clin Radiol. 2016. PMID: 26706231

10

OXPHOS-dependent metabolic reprogramming prompts metastatic potential of breast cancer cells under osteogenic differentiation.

Hu Y, Xu W, Zeng H, He Z, Lu X, Zuo D, Qin G, Chen W. Hu Y, et al. Among authors: he z. Br J Cancer. 2020 Nov;123(11):1644-1655. doi: 10.1038/s41416-020-01040-y. Epub 2020 Sep 16. Br J Cancer. 2020. PMID: 32934344 Free PMC article.

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