Deepthi Rajashekar

According to our database, Deepthi Rajashekar authored at least 11 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.

Alejandro Gutierrez, Anup Tuladhar, Matthias Wilms, Deepthi Rajashekar, Michael D. Hill, Andrew Demchuk, Mayank Goyal, Jens Fiehler, Nils D. Forkert

Int. J. Comput. Assist. Radiol. Surg., May, 2023

2022

Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks.

Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert

Dataset, May, 2022

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging.

Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert

IEEE Trans. Medical Imaging, 2022

A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder.

Emma A. M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

Stroke lesion localization in 3D MRI datasets with deep reinforcement learning.

Samuel Robertson, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.

Alejandro Gutierrez, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

2021

Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder.

Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sönke Langner, Nils Daniel Forkert

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows.

Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert

Proceedings of the Machine Learning in Clinical Neuroimaging - 4th International Workshop, 2021

2020

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks.

Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert

IEEE Access, 2020

Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows.

Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert

Proceedings of the Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2020

2016

Smart Phone User Behaviour Characterization Based on Autoencoders and Self Organizing Maps.

Deepthi Rajashekar, Nur Zincir-Heywood, Malcolm I. Heywood

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016