Dr. Shabnam Samankan Profile

Dr. Shabnam Samankan

at George Washington Univ

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Publications (1)

Proceedings Article | 3 April 2024 Presentation + Paper

Federated learning in computational pathology: classification of tall cell patterns in papillary thyroid carcinoma

Sonal Shukla, Margaret Brandwein-Weber, Shabnam Samankan, Ahmed Ayad, Mohamed Rabie, Scott Doyle

Proceedings Volume 12933, 129330U (2024) https://doi.org/10.1117/12.3006890

KEYWORDS: Data modeling, Tumor growth modeling, Pathology, Data privacy, Artificial intelligence, Thyroid, Deep learning, Performance modeling, Computer vision technology, Machine learning, Image classification, Computer aided detection

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This paper presents an exploration of Federated Learning (FL) in medical imaging, focusing on Computational Pathology (CP) with Whole Slide Images (WSIs) for head and neck cancer. While previous FL approaches in healthcare targeted radiology, genetics, and Electronic Health Records (EHRs), our research addresses the understudied area of CP datasets. Our aim is to develop robust AI models for CP datasets without sacrificing data privacy and security. To this end, we demonstrate the use of FL applied to a CP dataset consisting of papillary thyroid carcinoma, specifically focusing on the rare and aggressive variant called Tall Cell Morphology (TCM). Patients with TCM require more aggressive treatment and rigorous follow-up due to its aggressiveness and increased recurrence rates. In this work, we perform a simulated FL training experiment by dividing a dataset into three virtual ”clients”. We locally train a Convolutional Neural Network (CNN), to classify patches of tissue labelled from the local WSI dataset as “tall” (expressing TCM) or “non-tall”. Models are then aggregated and convergence is ensured through the Federated Averaging (FedAVG) algorithm. The decentralized approach of FL creates a secure and privacy-preserving collaborative training environment, keeping individual client data local through horizontal data partitioning. This enables collective training of deep learning models on distributed data, benefiting from a diverse and rich dataset while safeguarding patient privacy. We compare the efficacy of the FL-trained model to a centralized model (trained using all ”client” data together) using accuracy, sensitivity, specificity, and F-1 score. Our findings indicate that the simulated FL models exhibit performance on par with or superior to centralized learning, achieving accuracy scores between 75-87%, while centralized learning attains an accuracy of 82%. This novel approach holds promise for revolutionizing computational pathology and contributing to more effective medical decision-making.

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