Spyridon Bakas

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

2020 · 1.350 Zit. · Scientific Reports

The Medical Segmentation Decathlon

2022 · 1.168 Zit. · Nature Communications

Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

2019 · 530 Zit. · Lecture notes in computer science

CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII

2023 · 409 Zit. · Insights into Imaging

Metrics reloaded: recommendations for image analysis validation

2024 · 380 Zit. · Nature Methods

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

2024 · 258 Zit. · Insights into Imaging

Understanding metric-related pitfalls in image analysis validation

2024 · 166 Zit. · Nature Methods

Federated benchmarking of medical artificial intelligence with MedPerf

2023 · 141 Zit. · Nature Machine Intelligence

Privacy preservation for federated learning in health care

2024 · 132 Zit. · Patterns

OpenFL: the open federated learning library

2022 · 120 Zit. · Physics in Medicine and Biology

Metrics reloaded: Recommendations for image analysis validation

2022 · 62 Zit. · arXiv (Cornell University)

OpenFL: An open-source framework for Federated Learning

2021 · 58 Zit. · arXiv (Cornell University)

The Federated Tumor Segmentation (FeTS) Challenge

2021 · 42 Zit. · arXiv (Cornell University)

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts

2024 · 24 Zit. · Radiology Artificial Intelligence

Understanding metric-related pitfalls in image analysis validation

2023 · 20 Zit. · IT University Of Copenhagen (IT University of Copenhagen)