Metin Gurcan

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Director, Senior Associate Dean for Artificial Intelligence

Wake Forest University

United States

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About

Metin Gurcan is the Director and Senior Associate Dean for Artificial Intelligence at Wake Forest University in the United States. His research interests encompass advanced methodologies in artificial intelligence, particularly as they relate to healthcare applications, including machine learning models and deep learning analysis in oncology and psychiatry. Recent articles authored by Professor Gurcan highlight significant advancements in understanding and predicting complex health conditions through innovative computational techniques.

Recent Grants

Grant: Open

Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learning

Open Date: 2023-05-05

Close Date: 2028-04-30

Grant: Close

Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images

Open Date: 2023-04-06

Close Date: 2025-03-31

Grant: Open

Culturally Augmented Learning In Biomedical Informatics Research (CALIBIR) Program

Open Date: 2022-09-15

Close Date: 2027-08-31

Grant: Close

Analytics & Machine-learning for Maternal-health Interventions (AMMI): A Cross-CTSA Collaboration

Open Date: 2022-07-22

Close Date: 2026-04-30

Grant: Close

Auto-Scope Software-Automated Otoscopy to Diagnose Ear Pathology

Open Date: 2018-09-21

Close Date: 2021-08-31

Articles (10)

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2025

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2025

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2025

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2024

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2024

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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

Collaborators (4)

Aaron Moberly

Vanderbilt University Medical Center

UNITED STATES

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Ziyu Su

Ohio State University

UNITED STATES

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Wendy Frankel

Emeritus Professor

Ohio State University

UNITED STATES

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Fei Xing

Wake Forest School of Medicine

UNITED STATES

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Metin Gurcan

Research Interests

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About

Recent Grants

Articles (10)

Building a Machine Learning Model to Predict Postpartum Depression from Electronic Health Records in a Tertiary Care Setting

Transcriptional Consequences of MeCP2 Knockdown and Overexpression in Mouse Primary Cortical Neurons

Tumor Bud Classification in Colorectal Cancer Using Attention-Based Deep Multiple Instance Learning and Domain-Specific Foundation Models

Deep Learning Model for Predicting Lung Adenocarcinoma Recurrence from Whole Slide Images

Machine Learning Analysis of RNA-Seq Data Identifies Key Gene Signatures and Pathways in Mpox Virus-Induced Gastrointestinal Complications Using Colon Organoid Models

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS

NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images

Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review.

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images

Collaborators (4)

Aaron Moberly

Ziyu Su

Wendy Frankel

Fei Xing

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