Fatemeh Zabihollahy

Orcid: 0000-0003-3362-1009

According to our database1, Fatemeh Zabihollahy authored at least 10 papers between 2018 and 2024.

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

Timeline

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Links

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Bibliography

2024
Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI.
Int. J. Comput. Assist. Radiol. Surg., November, 2024

2022
A deep learning-based clinical target volume segmentation in female pelvic MRI for radiation therapy planning.
Proceedings of the Medical Imaging 2022: Image-Guided Procedures, 2022

Transfer learning based fully automated kidney segmentation on MR images.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

2021
Deep learning-based detection of COVID-19 from chest x-ray images.
Proceedings of the Medical Imaging 2021: Biomedical Applications in Molecular, 2021

2020
Patch-Based Convolutional Neural Network for Differentiation of Cyst From Solid Renal Mass on Contrast-Enhanced Computed Tomography Images.
IEEE Access, 2020

2019
Machine learning-based approach for fully automated segmentation of muscularis propria from histopathology images of intestinal specimens.
Proceedings of the Medical Imaging 2019: Digital Pathology, 2019

Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019

Fully-automated segmentation of optic disk from retinal images using deep learning techniques.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019

Deep learning based approach for fully automated detection and segmentation of hard exudate from retinal images.
Proceedings of the Medical Imaging 2019: Biomedical Applications in Molecular, 2019

2018
Myocardial scar segmentation from magnetic resonance images using convolutional neural network.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018


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