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Ibsa Jalata Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
5 h-index
11 pubs
78 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Ibsa Jalata's research focuses on the application of deep learning techniques to medical imaging and related challenges. His work includes developing unsupervised deep learning architectures for accelerated magnetic resonance imaging, often integrating system models with image priors. Jalata also investigates methods for semi-supervised medical image segmentation, utilizing augmentation techniques like Puzzlemix and Cut-Puzzle mix, sometimes without requiring segmentation masks. His research also extends to addressing data scarcity issues in deep learning for magnetic resonance image reconstruction through systematic exploitation of oversampling. Jalata has collaborated with researchers at the University of Arkansas at Fayetteville, including Ukash Nakarmi and Khoa Luu, on multiple publications. His work has resulted in 11 publications and 77 citations, with an h-index of 5. He remains an active researcher, with his most recent publication in 2024.
Metrics
- h-index: 5
- Publications: 11
- Citations: 78
Selected Publications
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Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique (2024)
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Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks (2024)
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Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction (2024)
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When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging (2023)
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EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
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Non-volume preserving-based fusion to group-level emotion recognition on crowd videos (2022)
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Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network (2021)
Collaboration Network
Top Collaborators
Ukash Nakarmi
University of Arkansas at Fayetteville
5 shared publications
In database
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
- When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging
- When System Model meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging
- Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks
- Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique
Khoa Luu
University of Arkansas at Fayetteville
3 shared publications
In database
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Thanh-Dat Truong
University of Arkansas at Fayetteville
2 shared publications
In database
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Kha Gia Quach
Concordia University (CA)
1 shared publication
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
Ngan Le
University of Arkansas at Fayetteville (US)
1 shared publication
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
Chi Nhan Duong
Concordia University (CA)
1 shared publication
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
Kaushik Roy
North Carolina Agricultural and Technical State University (US)
1 shared publication
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
Jessica L. Allen
West Virginia University (US)
1 shared publication
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
Han‐Seok Seo
University of Arkansas at Fayetteville
1 shared publication
In database
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
Naga Venkata Sai Raviteja Chappa
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Pierce Helton
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Chase Rainwater
University of Arkansas at Fayetteville
1 shared publication
In database
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
Reeshad Khan
University of Arkansas at Fayetteville (US)
1 shared publication
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
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