Based on the evaluation of the MLCN 2020 scientific committee members on the scientific content, significance of the contribution, and clarity of the communication, the MLCN2020 best paper award is presented to Naresh Nandakumar and his coauthors Niharika S. D’Souza, Komal Manzoor, Jay Pillai, Sachin Gujar, Haris Sair, and Archana Venkataraman for the paper entitled “A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity”.
Naresh is currently a 4th year PhD student in the Neural Systems Analysis lab at the Johns Hopkins University. He received his Bachelor’s from Vanderbilt University, where he did research in the MASI lab. His research interests lie at the intersection of statistical modeling/deep learning and clinical neuroimaging. He has presented at ISBI 2018, MICCAI 2018, MICCAI 2019 and MICCAI 2020. He develops machine learning models to tackle clinically relevant problems. Currently, he is working on eloquent cortex detection of brain tumor patients using resting-state fMRI connectivity. He is always eager to speak with prospective collaborators, you can reach him at email@example.com or on his LinkedIn.
The winner is selected among 18 accepted papers for the presentation at the MLCN 2020. The runner-up papers include:
- Jianyuan Zhang, Feng Shi, Lei Chen, Zhong Xue, Lichi Zhang, and Dahong Qian: “Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning“
- Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, and Nils D. Forkert: “Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows“
- Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz: “Surface Agnostic Metrics for Cortical Volume Segmentation and Regression“
- Yannick Suter, Urspeter Knecht, Roland Wiest, Ekkehard Hewer, Philippe Schucht, and Mauricio Reyes: “Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning“