Arkadiusz Gertych

Associate Professor

Cedars-Sinai Medical Center

United States

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Dr. Arkadiusz Gertych is an Associate Professor at Cedars-Sinai Medical Center in the United States. His research focuses on the application of artificial intelligence in healthcare, particularly in the areas of kidney diseases, cancer pathology, and tissue analysis. Recent publications highlight advancements in AI-driven assessments, deep learning interpretability, and novel methodologies for improving diagnostic accuracy.

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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2023

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2023

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2022

Collaborators (5)

Dmitriy Sheyn

PI / Assistant professor

Cedars-Sinai Medical Center

UNITED STATES

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Mariusz Marzec

Assistant Professor

University of Silesia

POLAND

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Bartłomiej Pyciński

Silesian University of Technology

POLAND

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Julie Chan

Assistant Professor

University of Florida

UNITED STATES

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Massimo Salvi

Polytechnic University of Turin

ITALY

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Arkadiusz Gertych

Research Interests

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Articles (10)

AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases

Explainability and uncertainty: Two sides of the same coin for enhancing the interpretability of deep learning models in healthcare

Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular Testing

Immunohistochemistry annotations enhance AI identification of lymphocytes and neutrophils in digitized H&E slides from inflammatory bowel disease

Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization

The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients

iPSC-derived tenocytes seeded on microgrooved 3D printed scaffolds for Achilles tendon regeneration

Multilayer outperforms single-layer slide scanning in AI-based classification of whole slide images with low-burden acid-fast mycobacteria (AFB)

Tu1974 COMPUTATIONAL PATHOLOGY APPROACH TO IDENTIFY MICRO- AND MACROSCOPIC FEATURES OF UNINVOLVED ILEUM PREDICTIVE OF POSTOPERATIVE RECURRENCE (POR) IN CROHN'S DISEASE (CD)

Efficient automatic 3D segmentation of cell nuclei for high-content screening

Collaborators (5)

Dmitriy Sheyn

Mariusz Marzec

Bartłomiej Pyciński

Julie Chan

Massimo Salvi

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