Roshan Prakash Rane

Roshan Prakash Rane
Charité Universitätsmedizin Berlin | Charité · Department of Psychiatry and Psychotherapy

Doctor of Philosophy

About

7
Publications
2,019
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18
Citations
Citations since 2017
7 Research Items
18 Citations
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201720182019202020212022202302468
201720182019202020212022202302468
201720182019202020212022202302468

Publications

Publications (7)
Article
Full-text available
Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 - 78% in the IMAGEN dataset (n ~1182). Our results not only show that structural d...
Preprint
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Alcohol misuse during adolescence (AAM) has been linked with disruptive structural development of the brain and alcohol use disorder. Using machine learning (ML), we analyze the link between AAM phenotypes and adolescent brain structure (T1-weighted imaging and DTI) at ages 14, 19, and 22 in the IMAGEN dataset (n~1182). ML predicted AAM at age 22 f...
Preprint
Full-text available
The domain of medical imaging, especially brain Magnetic Resonance Imaging (sMRI), suffers from limited availability of labelled data. In this paper, we study how to effectively perform transfer learning from a large generic sMRI dataset to a small dataset of specific neurological disorder. We highlight the major challenges of transfer learning and...
Preprint
Full-text available
The PredNet architecture by Lotter et al. combines a biologically plausible architecture called Predictive Coding with self-supervised video prediction in order to learn the complex structureof the visual world. While the architecture has drawn a lot of attention and various extensions of the model exist, there is a lack ofa critical analysis. We f...

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Projects

Project (1)
Project
Experimenting with different techniques that would enable us to use deep learning on MRI datasets with few data. The different techniques include (1) using unsupervised training i.e. reconstruction (2) training on smaller chunks of the whole image (3) Using different MRI sequences like T1-weighed and T2-weighed