Anup Tuladhar

Orcid: 0000-0002-3942-2732

According to our database1, Anup Tuladhar authored at least 14 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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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

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

Modeling Neurodegeneration in silico With Deep Learning.
Frontiers Neuroinformatics, 2021

Federated Learning Using Variable Local Training for Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets.
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


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