Anup Tuladhar
Orcid: 0000-0002-3942-2732
According to our database1,
Anup Tuladhar
authored at least 14 papers
between 2020 and 2023.
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Bibliography
2023
Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.
Int. J. Comput. Assist. Radiol. Surg., May, 2023
Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.
Neuroinformatics, January, 2023
2022
Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks.
Dataset, May, 2022
An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.
J. Am. Medical Informatics Assoc., 2022
Investigating the Vulnerability of Federated Learning-Based Diabetic Retinopathy Grade Classification to Gradient Inversion Attacks.
Proceedings of the Ophthalmic Medical Image Analysis - 9th International Workshop, 2022
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
Simulating progressive neurodegeneration in silico with deep artificial neural networks.
Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022
2021
An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks.
Sensors, 2021
Frontiers Neuroinformatics, 2021
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021
2020
Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling.
J. Biomed. Informatics, 2020
Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks.
IEEE Access, 2020