Wei-kai Lee

155, Sec. 2, Li Nong St. Beitou Dist, 112304
Beitou, TPQ 11230, Taiwan

Last Updated: 09/26/2026

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Found 12 publications

AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.

AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.

Journal: Biomedicines

Published: May 23, 2026

Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Journal: Neuroinformatics

Published: October 02, 2024

Analysis of the difference between Alzheimer's disease, mild cognitive impairment and normal people by using fractal dimensions and small-world network.

Analysis of the difference between Alzheimer's disease, mild cognitive impairment and normal people by using fractal dimensions and small-world network.

Journal: Progress in brain research

Published: April 21, 2024

Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.

Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.

Journal: Mayo Clinic proceedings. Digital health

Published: February 04, 2024

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

Published: July 20, 2023

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

Journal: Journal of magnetic resonance imaging : JMRI

Published: May 18, 2023

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

Journal: Journal of magnetic resonance imaging : JMRI

Published: October 14, 2022

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images.

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images.

Journal: Computer methods and programs in biomedicine

Published: October 11, 2022

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Journal: Scientific reports

Published: April 26, 2020

Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics.

Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

Published: April 04, 2020

Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

Journal: Artificial intelligence in medicine

Published: December 05, 2019

Achieving Nearly 30% External Quantum Efficiency for Orange-Red Organic Light Emitting Diodes by Employing Thermally Activated Delayed Fluorescence Emitters Composed of 1,8-Naphthalimide-Acridine Hybrids.

Achieving Nearly 30% External Quantum Efficiency for Orange-Red Organic Light Emitting Diodes by Employing Thermally Activated Delayed Fluorescence Emitters Composed of 1,8-Naphthalimide-Acridine Hybrids.

Journal: Advanced materials (Deerfield Beach, Fla.)

Published: August 30, 2017

Showing 1-12 of 12

Last Updated: 09/26/2026