To propose a federated learning (FL) framework incorporating a novel deep ensemble strategy for multi-institutional brain metastasis (BM) segmentation, improving performance in limited local datasets while preserving privacy by avoiding large-scale data trans...
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Jingtong Zhao
Duke University
A Federated Learning Scheme Based on Deep Ensemble Learning for MRI-Based Multi-Institutional Study of Brain Metastasis Segmentation
Poster Program · Therapy Physics
Quantum-Inspired Potential Mapping for Multi-Parametric MRI Radiomics–Based Post-Resection Glioblastoma Survival Prediction
To develop a multi-parametric MRI (mp-MRI) radiomics framework for predicting post-resection glioblastoma (GBM) survival by integrating conventional MR modalities with a quantum mechanics–inspired imaging representation.
Poster Program · Diagnostic and Interventional Radiology Physics
Uncertainty-Guided Federated Learning for Robust Multi-Institutional MRI-Based Brain Metastasis Segmentation
To develop and evaluate a federated learning (FL) framework for brain metastasis (BM) segmentation that integrates an uncertainty score into a novel FL objective, improving segmentation robustness and potentially performance when training on limited-size data...
Poster Program · Diagnostic and Interventional Radiology Physics
Improving Pediatric Glioma Segmentation on Multi-Parametric MRI Via Federated Learning Using Adult Patient Data: A Benchmark Study
To evaluate whether a federated learning (FL) scheme that leverages adult glioma patient data improves multi-parametric MRI (mp-MRI) based pediatric glioma segmentation.
Proffered Program · Therapy Physics