James Dolezal

Assistant Professor

Geisinger Medical Center

United States

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James Dolezal is an Assistant Professor at Geisinger Medical Center in the United States. His research primarily focuses on the integration of artificial intelligence and deep learning in pathology, particularly for cancer detection and classification. His recent publications include studies on generative adversarial networks and their applications in analyzing histological features across various cancers. Additionally, he explores the impact of digital histopathology on predictive modeling in oncology.

Articles (9)

Year:

2024

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2024

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2023

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2023

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2022

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2022

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2022

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2021

Year:

2021

Collaborators (8)

Saman Warnakulasuriya

Professor

King’s College London

UNITED KINGDOM

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Charles Perou

Professor

University of North Carolina

UNITED STATES

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Syed Ali Khurram

Professor and Consultant Pathologist

University of Sheffield

UNITED KINGDOM

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Frederick M. Howard

UNITED STATES

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Hanna Hieromnimon

University of Chicago

UNITED STATES

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Ari Rosenberg

University of Chicago Medicine

UNITED STATES

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Heather Chen-Yost

University of Michigan

UNITED STATES

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Galina Khramtsova

University of Chicago

UNITED STATES

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James Dolezal

Research Interests

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Articles (9)

Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features

Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review

Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Induction Chemotherapy in Low-Risk HPV+ Oropharyngeal Cancer

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology

The use of artificial intelligence with uncertainty estimation to predict lung cancer relapse from histopathology.

The impact of site-specific digital histology signatures on deep learning model accuracy and bias.

Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features

Collaborators (8)

Saman Warnakulasuriya

Charles Perou

Syed Ali Khurram

Frederick M. Howard

Hanna Hieromnimon

Ari Rosenberg

Heather Chen-Yost

Galina Khramtsova

Make your next move.