The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research
Open accessAbstract
Objective. De-centralized data analysis becomes an increasingly preferred option in the healthcare domain, as it alleviates the need for sharing primary patient data across collaborating institutions. This highlights the need for consistent harmonized data curation, pre-processing, and identification of regions of interest based on uniform criteria. Approach. Towards this end, this manuscript describes the Fe derated T umor S egmentation (FeTS) tool, in terms of software architecture and functionality. Main results. The primary aim of the FeTS tool is to facilitate this harmonized processing and the generation of gold standard reference labels for tumor sub-compartments on brain magnetic resonance imaging, and further enable federated training of a tumor sub-compartment delineation model across numerous sites distributed across the globe, without the need to share patient data. Significance. Building upon existing open-source tools such as the Insight Toolkit and Qt, the FeTS tool is designed to enable training deep learning models targeting tumor delineation in either centralized or federated settings. The target audience of the FeTS tool is primarily the computational researcher interested in developing federated learning models, and interested in joining a global federation towards this effort. The tool is open sourced at https://github.com/FETS-AI/Front-End .
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Varying characteristics of the multi-parametric MRI data due to scanner hardware and acquisition protocol differences were handled at each collaborating site via established harmonized preprocessing pipelines.
“Moreover, varying characteristics of the mpMRI data due to scanner hardware and acquisition protocol differences33,34 were handled at each collaborating site via established harmonized preprocessing pipelines35–39.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [39]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
The 2021 WHO classification of tumors of the central nervous system: a summary
Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H. K., Pfister, S. M., Reifenberger, G., et al. (2021) · Neuro-Oncology · doi:10.1093/neuonc/noab106 · Cited by 12,273 · see citing works
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477 · see citing works
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
Self-citationN4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,119 · see citing works
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731 · see citing works
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652 · see citing works
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · see citing works
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 · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
Self-citationBakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
Self-citationGeneralised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_28 · Cited by 2,829 · see citing works
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072 · see citing works
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 · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Self-citationBakas, 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 · doi:10.17863/cam.38755 · Cited by 1,441 · see citing works
Self-citationMorphometric analysis of white matter lesions in MR images: method and validation
Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., & Palmer, A. C. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363096 · Cited by 1,268 · see citing works
3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support
Kikinis, R., Pieper, S., & Vosburgh, K. G. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7657-3_19 · Cited by 1,175 · see citing works
The Importance of Skip Connections in Biomedical Image Segmentation
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., & Pal, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_19 · Cited by 1,114 · see citing works
The first step for neuroimaging data analysis: DICOM to NIfTI conversion
Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.03.001 · Cited by 1,073 · see citing works
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9269-5 · Cited by 838 · see citing works
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609 · see citing works
Self-citationNiftyNet: a deep-learning platform for medical imaging
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552 · see citing works
The SRI24 multichannel atlas of normal adult human brain structure
Torsten Rohlfing, B., Zahr, N. M., Sullivan, E. V., & Pfefferbaum, A. (2009) · Human Brain Mapping · doi:10.1002/hbm.20906 · Cited by 477 · see citing works
The ANTsX ecosystem for quantitative biological and medical imaging
Tustison, N. J., Cook, P. A., Holbrook, A. J., Johnson, H. J., Muschelli, J., Devenyi, G. A., Duda, J., Das, S. R., Cullen, N. C., Gillen, D. L., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87564-6 · Cited by 472 · see citing works
Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy–Cramer, J., Kitamura, F., Pati, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02314 · Cited by 431 · see citing works
Self-citationThe Medical Imaging Interaction Toolkit
Ivo Wolf, A., Vetter, M., Wegner, I., Böttger, T., Nolden, M., Schöbinger, M., Hastenteufel, M., Kunert, T., & Meinzer, H.-P. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.04.005 · Cited by 424 · see citing works
Gutman, D. A., Cooper, L., Hwang, S. N., Holder, C. A., Gao, J., Aurora, T. D., Dunn, W. D., Scarpace, L. M., Mikkelsen, T., Jain, R., et al. (2013) · Radiology · doi:10.1148/radiol.13120118 · Cited by 417 · see citing works
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 · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Self-citationLung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community
Armato, S. G., McLennan, G., McNitt‐Gray, M. F., Meyer, C. R., Yankelevitz, D. F., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., et al. (2004) · Radiology · doi:10.1148/radiol.2323032035 · Cited by 381 · see citing works
Image segmentations produced by BAMF under the AIMI Annotations initiative
Van Oss, J., Murugesan, G. K., McCrumb, D., et al. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.12734644 · Cited by 367 · see citing works
Macyszyn, L. J., Akbari, H., Pisapia, J. M., Da, X., Attiah, M. A., Pigrish, V., Bi, Y., Pal, S., Davuluri, R. V., Roccograndi, L., et al. (2015) · Neuro-Oncology · doi:10.1093/neuonc/nov127 · Cited by 300 · see citing works
Self-citationLangerak, T., van der Heide, U. A., Kotte, A. N. T. J., Viergever, M. A., van Vulpen, M., & Pluim, J. P. W. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2057442 · Cited by 260 · see citing works
Torsten Rohlfing, B., Russakoff, D. B., & Maurer, C. R. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.830803 · Cited by 245 · see citing works
Horn, R. A., & Johnson, C. R. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511840371.006 · Cited by 243 · see citing works
Jain, R., Poisson, L., Gutman, D. A., Scarpace, L. M., Hwang, S. N., Holder, C. A., Wintermark, M., Rao, A. U. K., Colen, R. R., Kirby, J., et al. (2014) · Radiology · doi:10.1148/radiol.14131691 · Cited by 235 · see citing works
Mazzara, G. P., Velthuizen, R. P., Pearlman, J. L., Greenberg, H. M., & Wagner, H. (2004) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.01.026 · Cited by 216 · see citing works
Identification of Anonymous MRI Research Participants with Face-Recognition Software
Schwarz, C. G., Kremers, W. K., Therneau, T. M., Sharp, R. R., Gunter, J. L., Vemuri, P., Arani, A., Spychalla, A. J., Kantarci, K., Knopman, D. S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmc1908881 · Cited by 215 · see citing works
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 · doi:10.21037/cco.2017.06.28 · Cited by 213 · see citing works
Self-citationGlioblastoma Multiforme: A Look Inside Its Heterogeneous Nature
Inda, M.-D.-M., Bonavia, R., & Seoane, J. (2014) · Cancers · doi:10.3390/cancers6010226 · Cited by 208 · see citing works
Continuous Dice Coefficient: a Method for Evaluating Probabilistic SegmentationsPreprint
Shamir, R. R., Duchin, Y., Kim, J., Sapiro, G., & Harel, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/306977 · Cited by 206 · see citing works
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 · doi:10.1117/1.jmi.5.1.011018 · Cited by 204 · see citing works
Self-citationFederated Learning for Breast Density Classification: A Real-World Implementation
Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60548-3_18 · Cited by 202 · see citing works
OpenFL: the open federated learning library
Foley, P., Sheller, M., Edwards, B., Pati, S., Riviera, W., Sharma, M., Moorthy, P. N., Wang, S.-H., Martin, J., Mirhaji, P., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac97d9 · Cited by 194 · see citing works
Self-citationAldape, K., Amin, S. B., Ashley, D. M., Barnholtz‐Sloan, J. S., Bates, A. J., Beroukhim, R., Bock, C., Brat, D. J., Claus, E. B., Costello, J., et al. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy020 · Cited by 179 · see citing works
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176 · see citing works
Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation
McKinley, R., Meier, R., & Wiest, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11726-9_40 · Cited by 174 · see citing works
Habes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., Nasrallah, I., Launer, L. J., Rashid, T., Bilgel, M., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12178 · Cited by 173 · see citing works
Self-citationDigital Imaging and Communications in Medicine (DICOM): A Practical Introduction and Survival Guide
Peck, D. J. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.109.064592 · Cited by 169 · see citing works
Binder, Z. A., Thorne, A. H., Bakas, S., Wileyto, E. P., Bilello, M., Akbari, H., Rathore, S., Ha, S. M., Zhang, L., Ferguson, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.06.006 · Cited by 166 · see citing works
Self-citationAkbari, H., Macyszyn, L. J., Da, X., Bilello, M., Wolf, R. L., Martinez‐Lage, M., Biros, G., Alonso‐Basanta, M., O’Rourke, D. M., & Davatzikos, C. A. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001202 · Cited by 164 · see citing works
Self-citationDeeley, M. A., Chen, A., Datteri, R. D., Noble, J. H., Cmelak, A. J., Donnelly, E. F., Malcolm, A. W., Moretti, L., Jaboin, J. J., Niermann, K. J., et al. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/14/021 · Cited by 144 · see citing works
MedINRIA: Medical Image Navigation and Research Tool by INRIA
Toussaint, N., Souplet, J.-C., & Fillard, P. (2007) · Cited by 127 · see citing works
Imaging descriptors improve the predictive power of survival models for glioblastoma patients
Mazurowski, M. A., Desjardins, A., & Malof, J. M. (2013) · Neuro-Oncology · doi:10.1093/neuonc/nos335 · Cited by 114 · see citing works
Akbari, H., Bakas, S., Pisapia, J. M., Nasrallah, M. P., Rozycki, M., Martinez‐Lage, M., Morrissette, J. J. D., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy033 · Cited by 113 · see citing works
Self-citationOverview of the DICOM standard
Muštra, M., Delač, K., & Grgić, M. (2008) · International Symposium ELMAR · Cited by 107 · see citing works
Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review
Kazerooni, A. F., Bakas, S., Rad, H. S., & Davatzikos, C. A. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26907 · Cited by 104 · see citing works
Self-citationAkbari, H., Rathore, S., Bakas, S., Nasrallah, M. P., Shukla, G., Mamourian, E., Rozycki, M., Bagley, S. J., Rudie, J. D., Flanders, A. E., et al. (2020) · Cancer · doi:10.1002/cncr.32790 · Cited by 103 · see citing works
Self-citationThe Federated Tumor Segmentation (FeTS) ChallengePreprint
Pati, S., Baid, U., Zenk, M., Edwards, B., Sheller, M., Reina, G. A., Foley, P., Gruzdev, A., Martin, J., Albarqouni, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05874 · Cited by 102 · see citing works
Self-citationAkbari, H., Macyszyn, L. J., Da, X., Wolf, R. L., Bilello, M., Verma, R., O’Rourke, D. M., & Davatzikos, C. A. (2014) · Radiology · doi:10.1148/radiol.14132458 · Cited by 100 · see citing works
Self-citationThe 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 · doi:10.1007/978-3-030-46643-5_38 · Cited by 94 · see citing works
Self-citationDICOM and Radiology: Past, Present, and Future
Kahn, C. E., Carrino, J. A., Flynn, M. J., Peck, D. J., & Horii, S. C. (2007) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2007.06.004 · Cited by 93 · see citing works
Bakas, S., Akbari, H., Pisapia, J. M., Martinez‐Lage, M., Rozycki, M., Rathore, S., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-1871 · Cited by 89 · see citing works
Self-citationL 1 Approximation and the Analysis of Data
Barrodale, I. (1968) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2985267 · Cited by 84 · see citing works
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 · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84 · see citing works
Self-citationMulti-classifier framework for atlas-based image segmentation
Rohlfing, T., & Maurer, C. R. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.03.017 · Cited by 80 · see citing works
Bonekamp, D., Deike‐Hofmann, K., Wiestler, B., Wick, W., Bendszus, M., Radbruch, A., & Heiland, S. (2014) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24756 · Cited by 67 · see citing works
DLTK: State of the Art Reference Implementations for Deep Learning on Medical ImagesPreprint
Pawlowski, N., Ktena, S. I., Lee, M. C. H., Kainz, B., Rueckert, D., Glocker, B., & Rajchl, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06853 · Cited by 65 · see citing works
A review of Methods for Bias Correction in Medical Images
Song, S., Zheng, Y., & He, Y. (2017) · Biomedical Engineering Review · doi:10.18103/bme.v3i1.1550 · Cited by 62 · see citing works
Rathore, S., Bakas, S., Pati, S., Akbari, H., Kalarot, R., Sridharan, P., Rozycki, M., Bergman, M., Tunc, B., Verma, R., et al. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-75238-9_12 · Cited by 57 · see citing works
Self-citationAI-based prognostic imaging biomarkers for precision neuro-oncology: the ReSPOND consortium
Davatzikos, C. A., Barnholtz‐Sloan, J. S., Bakas, S., Colen, R. R., Mahajan, A., Balañá, C., Capellades, J., Puig, J., Jain, R., Sloan, A. E., et al. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa045 · Cited by 57 · see citing works
Self-citationGaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
Pati, S., Thakur, S. P., Hamamcı, İ. E., Baid, U., Baheti, B., Bhalerao, M., Güley, O., Mouchtaris, S., Lang, D., Thermos, S., et al. (2023) · Communications Engineering · doi:10.1038/s44172-023-00066-3 · Cited by 54 · see citing works
Self-citationOpenFL: An open-source framework for Federated LearningPreprint
Reina, G. A., Gruzdev, A., Foley, P., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06413 · Cited by 53 · see citing works
Self-citationBakas, S., Shukla, G., Akbari, H., Erus, G., Sotiras, A., Rathore, S., Sako, C., Ha, S. M., Rozycki, M., Shinohara, R. T., et al. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.3.031505 · Cited by 52 · see citing works
Self-citationOn the Use of Dependabot Security Pull Requests
Alfadel, M., Costa, D. E., Shihab, E., & Mkhallalati, M. (2021) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00037 · Cited by 46 · see citing works
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Beers, A. L., Brown, J. M., Chang, K., Hoebel, K., Patel, J., Ly, K. I., Tolaney, S. M., Brastianos, P. K., Rosen, B. R., Gerstner, E. R., et al. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09477-5 · Cited by 45 · see citing works
Pati, S., Verma, R., Akbari, H., Bilello, M., Hill, V. B., Sako, C., Correa, R., Beig, N. G., Venet, L., Thakur, S. P., et al. (2020) · Medical Physics · doi:10.1002/mp.14556 · Cited by 45 · see citing works
Self-citationBrain Tumor Segmentation Based on 3D Residual U-Net
Bhalerao, M., & Thakur, S. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46643-5_21 · Cited by 39 · see citing works
Self-citationSkull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning
Thakur, S. P., Doshi, J. J., Pati, S., Ha, S. M., Sako, C., Talbar, S. N., Kulkarni, U., Davatzikos, C. A., Erus, G., & Bakas, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_6 · Cited by 28 · see citing works
Self-citationLabel Fusion for Multi-atlas Segmentation Based on Majority Voting
Huo, J., Wang, G., Wu, Q. M. J., & Akilan, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20801-5_11 · Cited by 20 · see citing works
iGLASS: imaging integration into the Glioma Longitudinal Analysis Consortium
Bakas, S., Ormond, D. R., Alfaro-Munoz, K. D., Smits, M., Cooper, L. A. D., Verhaak, R. G. W., & Poisson, L. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa160 · Cited by 19 · see citing works
Self-citationPati, S., Thakur, S. P., Hamamcı, İ. E., et al. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.48550/arxiv.2103.01006 · Cited by 10 · see citing works
Self-citationBaid, U., Pati, S., Thakur, S. P., Edwards, B., Sheller, M., Martin, J., & Bakas, S. (2021) · Neuro-Oncology · doi:10.1093/neuonc/noab196.532 · Cited by 7 · see citing works
Self-citationKoike, T., Kin, T., Shiode, T., Takayanagi, S., Tanaka, S., Mukasa, A., & Saito, N. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy148.765 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Self-citationOpenFL: the open federated learning library
Foley, P., Sheller, M., Edwards, B., Pati, S., Riviera, W., Sharma, M., Moorthy, P. N., Wang, S.-H., Martin, J., Mirhaji, P., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac97d9 · Cited by 194 · see citing works
Self-citationLearning Federated Visual Prompt in Null Space for MRI Reconstruction
Feng, C.-M., Li, B., Xu, X., Liu, Y., Fu, H., & Zuo, W. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00779 · Cited by 57 · see citing works
Nanni, L., Bonato, B., & Bertoldo, A. (2025) · Sensors · doi:10.3390/s25061838 · Cited by 47 · see citing works
Federated Learning in Computer Vision
Shenaj, D., Rizzoli, G., & Zanuttigh, P. (2023) · IEEE Access · doi:10.1109/access.2023.3310400 · Cited by 45 · see citing works
Bakas, S., Vollmuth, P., Galldiks, N., Booth, T. C., Aerts, H. J. W. L., Bi, W. L., Wiestler, B., Tiwari, P., Pati, S., Baid, U., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00315-2 · Cited by 36 · see citing works
Self-citationRamakrishnan, D. S., Jekel, L., Chadha, S., Janas, A., Moy, H., Maleki, N., Sala, M., Kaur, M., Petersen, G. C., Merkaj, S., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03021-9 · Cited by 25 · see citing works
Self-citationA multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation
LaBella, D., Khanna, O., McBurney-Lin, S., Mclean, R., Nedelec, P., Rashid, A. S., Tahon, N. H., Altes, T. A., Baid, U., Bhalerao, R., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03350-9 · Cited by 25 · see citing works
Self-citationAccelerating Parkinson’s Disease drug development with federated learning approaches
Khanna, A., Adams, J., Antoniades, C., Bloem, B. R., Carroll, C., Cedarbaum, J., Cosman, J., Dexter, D. T., Dockendorf, M. F., Edgerton, J., et al. (2024) · npj Parkinson's Disease · doi:10.1038/s41531-024-00837-5 · Cited by 21 · see citing works
Zhou, Z., Qian, X., Hu, J., Chen, G., Zhang, C., Zhu, J., & Dai, Y. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-20 · Cited by 21 · see citing works
Truong, N. C. D., Yogananda, C. G. B., Wagner, B., Holcomb, J. M., Reddy, D. D., Saadat, N., Hatanpaa, K. J., Patel, T., Fei, B., Lee, M., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230218 · Cited by 21 · see citing works
Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizábal, A., Zimmerer, D., Gruzdev, A. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60466-1 · Cited by 19 · see citing works
Self-citationFederated learning in oncology: Bridging artificial intelligence innovation and privacy protection
Qi, X., Xu, T., Dang, C., Qi, Z., Meng, L., & Yu, H. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104154 · Cited by 14 · see citing works
The transformative role of Artificial Intelligence in genomics: Opportunities and challenges
Dara, M., Dianatpour, M., Azarpira, N., & Tanideh, N. (2025) · Gene Reports · doi:10.1016/j.genrep.2025.102314 · Cited by 12 · see citing works
Low field, high impact: democratizing MRI for clinical and research innovation
Jones, D. K., Alexander, D. C., Chetcuti, K., Cercignani, M., Donald, K. A., Griswold, M. A., Kopanoğlu, E., Lagunju, I., Obungoloch, J., Ogbole, G. I., et al. (2024) · BJR|Open · doi:10.1093/bjro/tzaf022 · Cited by 11 · see citing works
Federated learning framework integrating REFINED CNN and Deep Regression Forests
Nolte, D., Bazgir, O., Ghosh, S., & Pal, R. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad036 · Cited by 9 · see citing works
Li, H., Conte, G. M., Hu, Q., Anwar, S. M., Kofler, F., Ezhov, I., Leemput, K. V., Piraud, M., Diaz, M., Cole, B., et al. (2024) · PubMed · Cited by 9 · see citing works
Self-citationZhang, J., LaBella, D., Zhang, D., Houk, J. L., Rudie, J. D., Zou, H., Warman, P. I., Mazurowski, M. A., & Calabrese, E. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8580 · Cited by 8 · see citing works
Mahmoud, E., Gass, J., Dhemesh, Y., Greaser, J., Pogorzelski, K., Isufi, E., Garrett, F. G., Thacker, J., Tahon, N. H., Sinclair, J., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-06011-7 · Cited by 8 · see citing works
Toward Generalizable Multiple Sclerosis Lesion Segmentation Models
Badea, L., & Popa, M. (2025) · IEEE Access · doi:10.1109/access.2025.3576145 · Cited by 6 · see citing works
Bajunaid, R., Niu, C., Hambly, C., Liu, Z., Yamada, Y., Alemán-Mateo, H., Anderson, L. J., Arab, L., Baddou, I., Bandini, L. G., et al. (2025) · Nature Food · doi:10.1038/s43016-025-01175-2 · Cited by 6 · see citing works
The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations
Reddy, D. D., Saadat, N., Holcomb, J. M., Wagner, B. C., Truong, N. C. D., Bowerman, J., Hatanpaa, K. J., Patel, T. R., Pinho, M. C., Yu, F., et al. (2026) · Scientific Data · doi:10.1038/s41597-026-07274-4 · Cited by 6 · see citing works
Yu, J., Liu, Q., Xu, C., Zhou, Q., Xu, J., Zhu, L. Y., Chen, C., Zhou, Y., Xiao, B., Zheng, L. L., et al. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03029-0 · Cited by 5 · see citing works
An Overview of Open Source Deep Learning-Based Libraries for Neuroscience
Tshimanga, L. F., Pup, F. D., Corbetta, M., & Atzori, M. (2023) · Applied Sciences · doi:10.3390/app13095472 · Cited by 5 · see citing works
Liu, H., Wei, D., Dai, Q., Wu, X., Zheng, Y., & Wang, L. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103759 · Cited by 4 · see citing works
Moawad, A. W., Janas, A., Baid, U., Ramakrishnan, D. S., Saluja, R., Ashraf, N., Maleki, N., Jekel, L., Yordanov, N., Fehringer, P., et al. (2024) · PubMed · Cited by 4 · see citing works
Self-citationOptimization of deep learning models for inference in low resource environments
Thakur, S. P., Pati, S., Wu, J., Panchumarthy, R., Karkada, D., Kozlov, A. V., Shamporov, V. A., Suslov, A., Liakhov, D., Proshin, M., et al. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110615 · Cited by 3 · see citing works
Self-citationFederated learning on magnetic resonance imaging: a critical review
Markodimitrakis, E., Trivizakis, E., Ioannidis, G., Koutoulakis, E., Tsiknakis, M., Papanikolaou, N., Klontzas, M. E., Yaqub, M. K., & Kostas Marias, A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11508-7 · Cited by 3 · see citing works
Cascaded diffusion model and segment anything model for medical image synthesis
Pang, H., 洪晓明, Zhang, P., Zhu, P., Yao, S., An, F., Yang, G., Liu, T., Qiu, A., Ye, C., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114148 · Cited by 2 · see citing works
Poster: A Distributed Deep Reinforcement Learning System for Medical Image Segmentation
Xu, L. (2023) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3580252.3589997 · Cited by 1 · see citing works
Wu, C., Gupta, P., & Mohapatra, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00007-2 · Cited by 1 · see citing works
Truong, N. C. D., Yogananda, C. G. B., Wagner, B., Saadat, N., Holcomb, J. M., Reddy, D. D., Lohdi, S., Bowerman, J., Hatanpaa, K. J., Patel, T., et al. (2025) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.3049114 · Cited by 1 · see citing works
Categorical and phenotypic image synthetic learning as an alternative to federated learning
Truong, N. C. D., Yogananda, C. G. B., Wagner, B., Holcomb, J. M., Reddy, D. D., Saadat, N., Bowerman, J., Hatanpaa, K. J., Patel, T., Fei, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64385-z · Cited by 1 · see citing works
Barry, N., Kendrick, J., Molin, K., Li, S., Rowshanfarzad, P., Hassan, G. M., Dowling, J., Ong, J. S. L., Parizel, P. M., Hofman, M. S., et al. (2026) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae36e0 · Cited by 1 · see citing works
Synthesizing 3D multi-contrast brain tumor MRIs using tumor mask conditioning
Truong, N. C. D., Yogananda, C. G. B., Wagner, B., Holcomb, J. M., Reddy, D. D., Saadat, N., Hatanpaa, K. J., Patel, T., Fei, B., Lee, M., et al. (2024) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.3009331 · Cited by 1 · see citing works
Windmills in the brain: the radiomic pursuit of MGMT status in gliomas
Cao, H. (2024) · European Radiology · doi:10.1007/s00330-024-10858-6
Conte, G. M., Moassefi, M., Decker, P. A., Kosel, M., McCarthy, C. B., Sagen, J. A., Nikanpour, Y., Fereidan‐Esfahani, M., Ruff, M. W., Guido, F. S., et al. (2025) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8645
Collaborative evaluation for performance assessment of medical imaging applications
Kassem, H., Singh, A., Aristizábal, A., Bakas, S., Sheller, M., Umeton, R., Pati, S., Moorthy, P. N., & Karargyris, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00019-5
Self-citationInformatics at the Frontier of Cancer Research
Noller, K., Botsis, T., Cámara, P. G., Ciotti, L., Cooper, L. A. D., Goecks, J., Griffith, M., Haas, B. J., Ideker, T., Karchin, R., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-2829 · Cited by 1 · see citing works
Self-citationKopřivová, T., Dostál, M., Jůza, T., Vybíhal, V., Ovesná, P., Kozubek, M., & Keřkovský, M. (2025) · Journal of Neuroimaging · doi:10.1111/jon.70103
Farhat, M., Dagher, S. A., Ozkara, B. B., Ozcan, B. B., Karabacak, M., Chen, M., Yedavalli, V. S., & Wintermark, M. (2025) · The Neuroradiology Journal · doi:10.1177/19714009251407175
Lee, R. S. M., LaBella, D., Zhang, J., Magudia, K. A., & Calabrese, E. (2026) · American Journal of Neuroradiology · doi:10.3174/ajnr.a9217
Federated Learning Strategies for Image Analysis in the Healthcare Sector
Mora, H., Osuna, A. P., Maciá-Lillo, A., Martínez-García, A., & Szymański, J. (2025) · International Conference on Advances in Machine Intelligence and Cybersecurity Technologies (AMICT) · doi:10.1109/amict65811.2025.11402825
Melhem, A., AlEroud, A. F., Al-Mamun, A., Ibrahem, M. I., & Nasser, N. (2026) · ACM Transactions on the Web · doi:10.1145/3801730
Latent-Space Ensemble Synthesis of Missing Brain Tumor MRI Modalities for BraTS Challenge
Lathulerie, A. C., Abramova, V., Díaz, M. R., Estrada, U. M. L.-T., Yalçın, C., Hamadache, R. E., Lisazo, C., Casamitjana, A., Oliver, A., & Lladó, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16370-7_3
Moassefi, M., Decker, P. A., Conte, G. M., Kosel, M., Molinaro, A. M., Niederschweiberer, M. A., Nikanpour, Y., Ruff, M. W., Burns, T. C., Farooq, U., et al. (2026) · Cancer Research Communications · doi:10.1158/2767-9764.crc-25-0710
Šlachta, M., Halaj, M., Balazova, K., Kalita, O., Hrabálek, L., Čecháková, E., & Kudlová, P. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1797487
Abdallah, S. H., & Akhloufi, M. A. (2026) · Applied Sciences · doi:10.3390/app16157687
O’Brien, K., Murphy, L., & Doyle, S. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/m372713091
Tian, T., Zhang, S., Yang, X., & Shi, R. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1947584
Trust Fusion with Choquet-Based Interaction Aggregation in Federated Learning
Rachwał, A., Rachwał, A., & Karczmarek, P. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728518
- Authors
- Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., Martin, J., Shah, P., Menze, B., & Bakas, S.
- Affiliations
- Technical University of Munich (DE)
- University of Pennsylvania (US)
- Intel (United States) (US)
- Intel Corporation
- University of Zurich (CH)
- Corresponding author
- Bakas, Spyridon
- ORCID
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2022-10-12
- Volume
- 67
- Issue
- 20
- Pages
- 204002
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- PMC
- PMC9592188
- OpenAlex
- W4296794855
- Cited by
- 51
- References
- 84
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 12 · 2023: 6 · 2022: 2
- FWCI
- 3.25
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Cancer Institute (U01CA242871)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- federated learning, tumor segmentation, brain MRI, solid tumors, data privacy, data harmonization, open source software, Insight Toolkit, deep learning training
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Neoplasms, Software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
