Ankita Joshi

About

I am currently a Research Fellow at He Lab, Imaging Research Center. I received my PhD degree in Computer Science from the University of Georgia in 2022, and master degree in Artificial Intelligence in 2017. My research interests lie in developing computational methodologies for image understanding problems in the fields of medical image analysis and computer vision. I try to solve medical imaging problems using deep learning-based methods. I am also excited in interdisciplinary research, collaborating, and working with researchers from related fields.

Employment

• Cincinnati Children's Hospital Medical Center Postdoctoral Research Fellow
  2023 - Present

Education

• University of Georgia PhD
  2017 - 2022
• University of Georgia MS
  2015 - 2017
• University of Pune BS
  2009 - 2013

Projects & Funding

Projects & funding information is unavailable.

Publications (11)

• Mapping white matter microstructure at term age to motor outcomes at 2 years in very preterm infants: a multicentre cohort study
  Archives of Disease in Childhood - Fetal and Neonatal Edition 2026 DOI: 10.1136/archdischild-2025-328726
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• FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions
  Frontiers in Neuroscience 2026 DOI: 10.3389/fnins.2025.1734498
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• Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review
  Artificial Intelligence and Applications 2025 DOI: 10.47852/bonviewAIA52026392
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• Enhancing zero-shot classification in chest x-rays via cross-modal representation and attention alignment
  Multimodal Image Exploitation and Learning 2025 2025 DOI: 10.1117/12.3055633
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• A systematic review of automated methods to perform white matter tract segmentation
  Frontiers in Neuroscience 2024 DOI: 10.3389/fnins.2024.1376570
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• MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2023 DOI: 10.1007/978-3-031-45087-7_17
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• R2Net: Efficient and flexible diffeomorphic image registration using Lipschitz continuous residual networks
  Medical Image Analysis 2023 DOI: https://doi.org/10.1016/j.media.2023.102917
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• Diffeomorphic Image Registration Using Lipschitz Continuous Residual Networks
  Proceedings of The 5th International Conference on Medical Imaging with Deep Learning 2022
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• Efficient Diffeomorphic Image Registration using Multi-Scale Dual-Phased Learning
  Proceedings - International Symposium on Biomedical Imaging 2022 DOI: 10.1109/ISBI52829.2022.9761693
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• DDR-net: Dividing and downsampling mixed network for diffeomorphic image registration
  arXiv 2021 DOI: 10.48550/arxiv.2105.11361
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