| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1790 | 2018 |
| The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification U Baid, S Ghodasara, S Mohan, M Bilello, E Calabrese, E Colak, ... arXiv preprint arXiv:2107.02314, 2021 | 497 | 2021 |
| Federated learning enables big data for rare cancer boundary detection S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ... Nature communications 13 (1), 7346, 2022 | 132 | 2022 |
| Identifying the best machine learning algorithms for brain tumor segmentation S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... progression assessment, and overall survival prediction in the BRATS …, 2018 | 115 | 2018 |
| MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge R Verma, N Kumar, A Patil, NC Kurian, S Rane, S Graham, QD Vu, ... IEEE Transactions on Medical Imaging 40 (12), 3413-3423, 2021 | 113 | 2021 |
| A novel approach for fully automatic intra-tumor segmentation with 3D U-Net architecture for gliomas U Baid, S Talbar, S Rane, S Gupta, MH Thakur, A Moiyadi, N Sable, ... Frontiers in computational neuroscience 14, 10, 2020 | 87 | 2020 |
| Overall survival prediction in glioblastoma with radiomic features using machine learning U Baid, SU Rane, S Talbar, S Gupta, MH Thakur, A Moiyadi, A Mahajan Frontiers in computational neuroscience 14, 61, 2020 | 85 | 2020 |
| The federated tumor segmentation (fets) challenge S Pati, U Baid, M Zenk, B Edwards, M Sheller, GA Reina, P Foley, ... arXiv preprint arXiv:2105.05874, 2021 | 66 | 2021 |
| Deep learning radiomics algorithm for gliomas (drag) model: a novel approach using 3d unet based deep convolutional neural network for predicting survival in gliomas U Baid, S Talbar, S Rane, S Gupta, MH Thakur, A Moiyadi, S Thakur, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 66 | 2019 |
| LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation P Dutande, U Baid, S Talbar Biomedical signal processing and control 67, 102527, 2021 | 60 | 2021 |
| The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv 2021 U Baid, S Ghodasara, S Mohan, M Bilello, E Calabrese, E Colak, ... arXiv preprint arXiv:2107.02314, 2021 | 46 | 2021 |
| GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows S Pati, SP Thakur, İE Hamamcı, U Baid, B Baheti, M Bhalerao, O Güley, ... Communications Engineering 2 (1), 23, 2023 | 43 | 2023 |
| The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics S Bakas, C Sako, H Akbari, M Bilello, A Sotiras, G Shukla, JD Rudie, ... Scientific data 9 (1), 453, 2022 | 42 | 2022 |
| Detecting covid-19 and community acquired pneumonia using chest ct scan images with deep learning S Chaudhary, S Sadbhawna, V Jakhetiya, BN Subudhi, U Baid, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 42 | 2021 |
| Adam challenge: Detecting age-related macular degeneration from fundus images H Fang, F Li, H Fu, X Sun, X Cao, F Lin, J Son, S Kim, G Quellec, S Matta, ... IEEE transactions on medical imaging 41 (10), 2828-2847, 2022 | 38 | 2022 |
| The University of California San Francisco preoperative diffuse glioma MRI dataset E Calabrese, JE Villanueva-Meyer, JD Rudie, AM Rauschecker, U Baid, ... Radiology: Artificial Intelligence 4 (6), e220058, 2022 | 34 | 2022 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, 2022 | 33 | 2022 |
| Comparative study of k-means, gaussian mixture model, fuzzy c-means algorithms for brain tumor segmentation U Baid, S Talbar International Conference on Communication and Signal Processing 2016 (ICCASP …, 2016 | 33 | 2016 |
| The 1st agriculture-vision challenge: Methods and results MT Chiu, X Xu, K Wang, J Hobbs, N Hovakimyan, TS Huang, H Shi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 30 | 2020 |
| Federated benchmarking of medical artificial intelligence with MedPerf A Karargyris, R Umeton, MJ Sheller, A Aristizabal, J George, A Wuest, ... Nature Machine Intelligence 5 (7), 799-810, 2023 | 25 | 2023 |
Sanjay TalbarPimpri Chinchwad College of Engineering, PuneVerified email at sggs.ac.in
