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Items: 1 to 20 of 109

1.

Convolutional Neural Networks for the Detection of Diseased Hearts Using CT Images and Left Atrium Patches.

Dormer JD, Halicek M, Ma L, Reilly CM, Schreibmann E, Fei B.

Proc SPIE Int Soc Opt Eng. 2018 Feb;10575. pii: 1057530. doi: 10.1117/12.2293548. Epub 2018 Feb 27.

2.

Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.

Fu J, Yang Y, Singhrao K, Ruan D, Chu FI, Low DA, Lewis JH.

Med Phys. 2019 Sep;46(9):3788-3798. doi: 10.1002/mp.13672. Epub 2019 Jul 26.

PMID:
31220353
3.

3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT.

Hamidian S, Sahiner B, Petrick N, Pezeshk A.

Proc SPIE Int Soc Opt Eng. 2017;10134. pii: 1013409. doi: 10.1117/12.2255795. Epub 2017 Mar 3.

4.

Classification of CT brain images based on deep learning networks.

Gao XW, Hui R, Tian Z.

Comput Methods Programs Biomed. 2017 Jan;138:49-56. doi: 10.1016/j.cmpb.2016.10.007. Epub 2016 Oct 20.

PMID:
27886714
5.

Heart Chamber Segmentation from CT Using Convolutional Neural Networks.

Dormer JD, Ma L, Halicek M, Reilly CM, Schreibmann E, Fei B.

Proc SPIE Int Soc Opt Eng. 2018 Feb;10578. pii: 105782S. doi: 10.1117/12.2293554. Epub 2018 Mar 12.

6.

Ultrasound Segmentation of Rat Hearts Using Convolution Neural Networks.

Dormer JD, Guo R, Shen M, Jiang R, Wagner MB, Fei B.

Proc SPIE Int Soc Opt Eng. 2018 Feb;10580. pii: 105801A. doi: 10.1117/12.2293558. Epub 2018 Mar 6.

7.

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Ibragimov B, Xing L.

Med Phys. 2017 Feb;44(2):547-557. doi: 10.1002/mp.12045.

8.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

Yang X, Liu C, Wang Z, Yang J, Min HL, Wang L, Cheng KT.

Med Image Anal. 2017 Dec;42:212-227. doi: 10.1016/j.media.2017.08.006. Epub 2017 Aug 24.

PMID:
28850876
9.

Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

Ren X, Xiang L, Nie D, Shao Y, Zhang H, Shen D, Wang Q.

Med Phys. 2018 May;45(5):2063-2075. doi: 10.1002/mp.12837. Epub 2018 Mar 23.

10.

Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks.

Chowdhury NI, Smith TL, Chandra RK, Turner JH.

Int Forum Allergy Rhinol. 2019 Jan;9(1):46-52. doi: 10.1002/alr.22196. Epub 2018 Aug 11.

PMID:
30098123
11.
12.

MR-based synthetic CT generation using a deep convolutional neural network method.

Han X.

Med Phys. 2017 Apr;44(4):1408-1419. doi: 10.1002/mp.12155. Epub 2017 Mar 21.

PMID:
28192624
13.

Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images.

Liu M, Cheng D, Yan W; Alzheimer’s Disease Neuroimaging Initiative.

Front Neuroinform. 2018 Jun 19;12:35. doi: 10.3389/fninf.2018.00035. eCollection 2018.

14.

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks.

Qi Dou, Hao Chen, Lequan Yu, Lei Zhao, Jing Qin, Defeng Wang, Mok VC, Lin Shi, Pheng-Ann Heng.

IEEE Trans Med Imaging. 2016 May;35(5):1182-1195. doi: 10.1109/TMI.2016.2528129. Epub 2016 Feb 11.

PMID:
26886975
15.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network.

Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S.

IEEE Trans Med Imaging. 2016 May;35(5):1207-1216. doi: 10.1109/TMI.2016.2535865. Epub 2016 Feb 29.

PMID:
26955021
16.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

Huynh BQ, Li H, Giger ML.

J Med Imaging (Bellingham). 2016 Jul;3(3):034501. doi: 10.1117/1.JMI.3.3.034501. Epub 2016 Aug 22.

17.

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.

Daoud B, Morooka K, Kurazume R, Leila F, Mnejja W, Daoud J.

Comput Med Imaging Graph. 2019 Oct;77:101644. doi: 10.1016/j.compmedimag.2019.101644. Epub 2019 Jul 31.

PMID:
31426004
18.

3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT.

Wang S, Wang R, Zhang S, Li R, Fu Y, Sun X, Li Y, Sun X, Jiang X, Guo X, Zhou X, Chang J, Peng W.

Quant Imaging Med Surg. 2018 Jun;8(5):491-499. doi: 10.21037/qims.2018.06.03.

19.

Three-Class Mammogram Classification Based on Descriptive CNN Features.

Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A.

Biomed Res Int. 2017;2017:3640901. doi: 10.1155/2017/3640901. Epub 2017 Jan 15.

20.

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.

Yasaka K, Akai H, Abe O, Kiryu S.

Radiology. 2018 Mar;286(3):887-896. doi: 10.1148/radiol.2017170706. Epub 2017 Oct 23.

PMID:
29059036

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