Search Results

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

• Authors : Estudillo Romero A; Laboratoire Traitement du Signal et de l'Image (LTSI, INSERM UMR 1099), Université de Rennes, Rennes, France.; Migliaccio R

Subjects: Frontotemporal Dementia*/Frontotemporal Dementia*/Frontotemporal Dementia*/diagnostic imaging ; Frontotemporal Dementia*/Frontotemporal Dementia*/Frontotemporal Dementia*/diagnosis ; Frontotemporal Dementia*/Frontotemporal Dementia*/Frontotemporal Dementia*/physiopathology

• Source: International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2024 Dec; Vol. 19 (12), pp. 2339-2349. Date of Electronic Publication: 2024 Jun Publisher: Springer Country of Publication: Germany NLM ID: 101499225 Publication Model: Print-Electronic Cited Medium:

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An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

• Authors : B A; Department of Electronics and Communication Engineering, KGiSL Institute of Technology, Coimbatore, Tamil Nadu, India. .; Kalirajan K

• Source: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Nov 15. Date of Electronic Publication: 2024 Nov 15.Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium:

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Video-based automatic hand hygiene detection for operating rooms using 3D convolutional neural networks.

• Authors : Kim M; Department of Biomedical Engineering, University of Ulsan College of Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, 05505, Republic of Korea.; Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, Republic of Korea.

Subjects: Neural Networks, Computer* ; Operating Rooms* ; Hand Hygiene*/Hand Hygiene*/Hand Hygiene*/methods

• Source: Journal of clinical monitoring and computing [J Clin Monit Comput] 2024 Oct; Vol. 38 (5), pp. 1187-1197. Date of Electronic Publication: 2024 Jun 19.Publisher: Springer Country of Publication: Netherlands NLM ID: 9806357 Publication Model: Print-Electronic Cited Medium:

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EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning.

• Authors : Bagherzadeh S; Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.; Shalbaf A

• Source: Cognitive neurodynamics [Cogn Neurodyn] 2024 Oct; Vol. 18 (5), pp. 2767-2778. Date of Electronic Publication: 2024 May 09.Publisher: Springer Country of Publication: Netherlands NLM ID: 101306907 Publication Model: Print-Electronic Cited Medium:

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Comparative Analysis of Convolutional Neural Networks for Classification of Breast Abnormalities.

• Authors : Diaz-Escobar, J.; Kober, V. I.; Karnaukhov, V. N.

• Source: Journal of Communications Technology and Electronics. 2023, Vol. 68, Issue 12, pages. 7

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SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images.

• Authors : Geroski, Tijana; Pavic, Ognjen; Dasic, Lazar

• Source: Journal of Big Data. December, 2024, Vol. 11 Issue 1.

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3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer's disease sub-scores.

• Authors : Ning, Kaida; Cannon, Pascale B.; Yu, Jiawei

• Source: Brain Informatics. December, 2024, Vol. 11 Issue 1.

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Improved quantitative parameter estimation for prostate T 2 relaxometry using convolutional neural networks.

• Authors : Bolan PJ; Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA. .; Department of Radiology, University of Minnesota, Minneapolis, MN, USA. .

Subjects: Neural Networks, Computer* ; Prostate*/Prostate*/Prostate*/diagnostic imaging ; Magnetic Resonance Imaging*/Magnetic Resonance Imaging*/Magnetic Resonance Imaging*/methods

• Source: Magma (New York, N.Y.) [MAGMA] 2024 Aug; Vol. 37 (4), pp. 721-735. Date of Electronic Publication: 2024 Jul 23.Publisher: Springer Country of Publication: Germany NLM ID: 9310752 Publication Model: Print-Electronic Cited Medium:

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Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

• Authors : Kafali SG; Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.; Department of Bioengineering, University of California, Los Angeles, CA, USA.

Subjects: Magnetic Resonance Imaging*/Magnetic Resonance Imaging*/Magnetic Resonance Imaging*/methods ; Imaging, Three-Dimensional*/Imaging, Three-Dimensional*/Imaging, Three-Dimensional*/methods ; Neural Networks, Computer*

• Source: Magma (New York, N.Y.) [MAGMA] 2024 Jul; Vol. 37 (3), pp. 491-506. Date of Electronic Publication: 2024 Feb 01.Publisher: Springer Country of Publication: Germany NLM ID: 9310752 Publication Model: Print-Electronic Cited Medium:

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Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

• Authors : Huang YL; Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.; Lin YR

Subjects: Magnetic Resonance Spectroscopy*/Magnetic Resonance Spectroscopy*/Magnetic Resonance Spectroscopy*/methods ; Brain*/Brain*/Brain*/diagnostic imaging ; Brain*/Brain*/Brain*/metabolism

• Source: Magma (New York, N.Y.) [MAGMA] 2024 Jul; Vol. 37 (3), pp. 477-489. Date of Electronic Publication: 2023 Sep 15.Publisher: Springer Country of Publication: Germany NLM ID: 9310752 Publication Model: Print-Electronic Cited Medium:

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