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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.

R. Mehta, A. Filos, U. Baid, C. Sako, R. McKinley, M. Rebsamen, K. Dätwyler, R. Meier, P. Radojewski, G. Murugesan, S. Nalawade, C. Ganesh, B. Wagner, F. Yu, B. Fei, A. Madhuranthakam, J. Maldjian, L. Daza, C. Gómez, P. Arbeláez, C. Dai, S. Wang, H. Raynaud, Y. Mo, E. Angelini, Y. Guo, W. Bai, S. Banerjee, L. Pei, M. AK, S. Rosas-González, I. Zemmoura, C. Tauber, M. Vu, T. Nyholm, T. Löfstedt, L. Ballestar, V. Vilaplana, H. McHugh, G. Talou, A. Wang, J. Patel, K. Chang, K. Hoebel, M. Gidwani, N. Arun, S. Gupta, M. Aggarwal, P. Singh, E. Gerstner, J. Kalpathy-Cramer, N. Boutry, A. Huard, L. Vidyaratne, M. Rahman, K. Iftekharuddin, J. Chazalon, É. Puybareau, G. Tochon, J. Ma, M. Cabezas, X. Lladó, A. Oliver, L. Valencia, S. Valverde, M. Amian, M. Soltaninejad, A. Myronenko, A. Hatamizadeh, X. Feng, Q. Dou, N. Tustison, C. Meyer, N. Shah, S. Talbar, M. Weber, A. Mahajan, A. Jakab, R. Wiest, H. Fathallah-Shaykh, A. Nazeri, M. Milchenko, D. Marcus, A. Kotrotsou, R. Colen, J. Freymann, J. Kirby, C. Davatzikos, B. Menze, S. Bakas, Y. Gal, and T. Arbel. CoRR, (2021)

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Detection of Pathological Myopia and Optic Disc Segmentation with Deep Convolutional Neural Networks.U. Baid, B. Baheti, P. Dutande, and S. Talbar. TENCON, page 1345-1350. IEEE, (2019)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, R. Shinohara, C. Berger, S. Ha, M. Rozycki and 41 other author(s). CoRR, (2018)QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.R. Mehta, A. Filos, U. Baid, C. Sako, R. McKinley, M. Rebsamen, K. Dätwyler, R. Meier, P. Radojewski, G. Murugesan and 82 other author(s). CoRR, (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, M. Thakur, A. Moiyadi, S. Thakur, and A. Mahajan. BrainLes@MICCAI (2), volume 11384 of Lecture Notes in Computer Science, page 369-379. Springer, (2018)Brain Tumor Segmentation Based on Non Negative Matrix Factorization and Fuzzy Clustering.U. Baid, S. Talbar, and S. Talbar. BIOIMAGING, page 134-139. SciTePress, (2017)The 1st Agriculture-Vision Challenge: Methods and Results.M. Chiu, X. Xu, K. Wang, J. Hobbs, N. Hovakimyan, T. Huang, H. Shi, Y. Wei, Z. Huang, A. Schwing and 32 other author(s). CVPR Workshops, page 212-218. Computer Vision Foundation / IEEE, (2020)

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.

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