Area of research
Radiology, Nuclear Medicine and Imaging · Genetics
Research interest
Dr. Spyridon Bakas is the Joshua Edwards Associate Professor at IU School of Medicine Department of Pathology and Laboratory Medicine and is the Inaugural Director of the Division of Computational Pathology. He also holds secondary appointments in the Department of Radiology and Imaging Sciences, the Department of Biostatistics and Health Data Science, the Department of Neurological Surgery, and the Department of Computer Science in the Luddy School of Informatics, Computing, and Engineering. Before joining IU, Dr. Bakas was with the Department of Pathology & Laboratory Medicine and the Department of Radiology at the Perelman School of Medicine of the University of Pennsylvania (UPenn), and a secondary affiliation with the Dept. of Bioengineering at the UPenn. His research interests focus on the development, application, and benchmarking of advanced computational algorithms in medical imaging, with the intention of improving disease assessment, quantification, and diagnosis in the current clinical practice. He has been leading projects on image quantification, radiogenomics, and federated learning, towards enabling treatment selection models customized on an individual patient basis, while addressing health disparities and inequities. Dr. Bakas has received grant funding from the National Cancer Institute of the National Institutes of Health, the National Science Foundation, the Abramson Cancer Center, and the Translational Biomedical Imaging Center of the Institute for Translational Medicine and Therapeutics of UPenn. He has co-authored >100 peer-reviewed manuscripts and >70 medical conference abstracts, with collaborators that span across academic ranks and disciplines. He is a founding board member of the MICCAI Society's Special Interest Group on Biomedical Image Analysis Challenges (SIG-BIAS), the Vice Chair for Benchmarking & Clinical Translation in the MLCommons’ Medical group, the co-lead of the AI-RANO working group, and has served as the organizer and chai
The BraTS-Africa Dataset: Expanding the Brain Tumor Segmentation Data to Capture African Populations
The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Response Assessment in Neuro-Oncology (RANO) 2009–2025: Broad scope and implementation—A progress report
Metrics reloaded: recommendations for image analysis validation
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Understanding metric-related pitfalls in image analysis validation
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements
A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation
Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study
CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII
Federated benchmarking of medical artificial intelligence with MedPerf
The Epigenetic Evolution of Glioma Is Determined by the <i>IDH1</i> Mutation Status and Treatment Regimen
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium
Understanding metric-related pitfalls in image analysis validation
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Why is the Winner the Best?
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Why is the winner the best?
The Medical Segmentation Decathlon
The Liver Tumor Segmentation Benchmark (LiTS)
Glioma progression is shaped by genetic evolution and microenvironment interactions
The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results
Biomedical image analysis competitions: The state of current participation practice
Federated Tumor Segmentation
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data