MingDe Lin

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Adjunct Associate Professor

Yale University School of Medicine

United States

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MingDe Lin is an Adjunct Associate Professor at Yale School of Medicine, USA. His research primarily focuses on advanced imaging techniques for cancer assessment, including volumetric measurements, machine learning applications in glioma grading, and the optimization of staging systems for hepatocellular carcinoma. He has contributed to several studies examining innovative methods for tumor response analysis and the use of artificial intelligence in clinical settings.

Recent Grants

Grant: Close

Quantitative Multimodal Image Guidance for Improved Liver Cancer Treatment

Open Date: 2016-08-01

Close Date: 2021-07-31

Grant: Close

See, Reach, Treat Tumor-Optimized Transarterial Chemoembolization Drug Delivery

Open Date: 2011-09-20

Close Date: 2016-07-31

Articles (10)

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2024

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2023

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2022

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2022

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2022

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2022

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2022

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2021

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2021

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2021

Collaborators (6)

Mario Strazzabosco

UNITED STATES

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Rafael Duran

SWITZERLAND

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Lawrence Staib

Yale University

UNITED STATES

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Lynn Jeanette Savic

Berlin Institute of Health at Charité - Universitätsmedizin Berlin

GERMANY

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Seyedmehdi Payabvash

Assistant Professor

Yale University

UNITED STATES

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Sanjay Aneja

Assistant Professor

Yale University School of Medicine

UNITED STATES

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MingDe Lin

Research Interests

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Contact this professor

About

Recent Grants

Articles (10)

Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment ofBRAFV600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial

Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities

Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI

Three-Dimensional Quantitative Tumor Response and Survival Analysis of Hepatocellular Carcinoma Patients Who Failed Initial Transarterial Chemoembolization: Repeat or Switch Treatment?

PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients

What's Needed to Bridge the Gap Between US FDA Clearance and Real-world Use of AI Algorithms

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning

Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver

Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy

Collaborators (6)

Mario Strazzabosco

Rafael Duran

Lawrence Staib

Lynn Jeanette Savic

Seyedmehdi Payabvash

Sanjay Aneja

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