Bakas, Spyridon

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 4,177

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 4,015

The Future of Digital Health with Federated Learning

Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., et al. (2020) · arXiv

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 3,495

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data

Radiomics and Machine Learning in Medical ImagingBrain Tumor Detection and Classification

Cited by 3,049

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

Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 1,646

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · arXiv

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2019) · arXiv

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Liver Tumor Segmentation Benchmark (LiTS)

Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,412

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

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

Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 609

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

Kocak, B., Baessler, B., Bakas, S., Cuocolo, R., Fedorov, A., Maier-Hein, L., Mercaldo, N., Müller, H., Orlhac, F., Pinto dos Santos, D., et al. (2023) · Insights into Imaging

Radiomics and Machine Learning in Medical ImagingRadiology practices and education

Cited by 471

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 394

Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017) · The Cancer Imaging Archive

Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical Imaging

Cited by 316

Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017) · The Cancer Imaging Archive

Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical Imaging

Cited by 290

Understanding metric-related pitfalls in image analysis validation

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 241

Advanced magnetic resonance imaging in glioblastoma: a review

Shukla, G., Alexander, G. S., Bakas, S., Nikam, R., Talekar, K., Palmer, J. D., & Shi, W. (2017) · Chinese Clinical Oncology

Glioma Diagnosis and TreatmentAdvanced Neuroimaging Techniques and Applications

Cited by 213

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Davatzikos, C., Rathore, S., Bakas, S., Pati, S., Bergman, M., Kalarot, R., Sridharan, P., Gastounioti, A., Jahani, N., Cohen, E., et al. (2018) · Journal of Medical Imaging

Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

Cited by 204

Federated benchmarking of medical artificial intelligence with MedPerf

Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., George, J., Wuest, A., Pati, S., Kassem, H., Zenk, M., Baid, U., et al. (2023) · Nature Machine Intelligence

Artificial Intelligence in Healthcare and EducationPrivacy-Preserving Technologies in Data

Cited by 188

Common Limitations of Image Processing Metrics: A Picture Story

Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et al. (2021) · arXiv

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 126

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., & Davatzikos, C. (2016) · Lecture Notes in Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 101

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Pati, S., Singh, A., Rathore, S., Gastounioti, A., Bergman, M., Ngo, P., Ha, S. M., Bounias, D., Minock, J., Murphy, G., et al. (2020) · Lecture Notes in Computer Science

Radiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

Cited by 94

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 84

Metrics reloaded: Recommendations for image analysis validation

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2022) · arXiv

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 59