Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning. - Siddhesh P. Thakur; Jimit Doshi; Sarthak Pati; Sung Min Ha; Chiharu Sako; Sanjay N. Talbar; Uday Kulkarni; Christos Davatzikos; Güray Erus; Spyridon Bakas (BrainLes@MICCAI (1), 2019)

Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols. - Spyridon Bakas; Gaurav Shukla; Hamed Akbari; Güray Erus; Aristeidis Sotiras; Saima Rathore; Chiharu Sako; Sung Min Ha; Martin Rozycki; Ashish Singh; Russell T. Shinohara; Michel Bilello; et al. (Image-Guided Procedures, 2020)

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. - Siddhesh P. Thakur; Jimit Doshi; Sarthak Pati; Saima Rathore; Chiharu Sako; Michel Bilello; Sung Min Ha; Gaurav Shukla; Adam E. Flanders; Aikaterini Kotrotsou; Mikhail Milchenko; Spencer Liem; et al. (NeuroImage, 2020)

Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. - Siddhesh P. Thakur; Sarthak Pati; Ravi Panchumarthy; Deepthi Karkada; Junwen Wu; Dmitry Kurtaev; Chiharu Sako; Prashant Shah; Spyridon Bakas (BrainLes@MICCAI (1), 2021)

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. - Ujjwal Baid; Satyam Ghodasara; Michel Bilello; Suyash Mohan; Evan Calabrese; Errol Colak; Keyvan Farahani; Jayashree Kalpathy-Cramer; Felipe C. Kitamura; Sarthak Pati; Luciano M. Prevedello; Jeffrey D. Rudie; et al. (CoRR, 2021)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. - Raghav Mehta; Angelos Filos; Ujjwal Baid; Chiharu Sako; Richard McKinley; Michael Rebsamen; Katrin Dätwyler; Raphael Meier; Piotr Radojewski; Gowtham Krishnan Murugesan; Sahil S. Nalawade; Chandan Ganesh; et al. (CoRR, 2021)

Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning. - Rachel Madhogarhia; Anahita Fathi Kazerooni; Sherjeel Arif; Jeffrey B. Ware; Ariana M. Familiar; Lorenna Vidal; Sina Bagheri; Hannah Anderson; Debanjan Haldar; Sophie Yagoda; Erin Graves; Michael Spadola; et al. (Computer-Aided Diagnosis, 2022)

Federated Learning Enables Big Data for Rare Cancer Boundary Detection. - Sarthak Pati; Ujjwal Baid; Brandon Edwards; Micah J. Sheller; Hans Shih-Han Wang; G. Anthony Reina; Patrick Foley; Alexey Gruzdev; Deepthi Karkada; Christos Davatzikos; Chiharu Sako; Satyam Ghodasara; et al. (CoRR, 2022)