Amirhossein Rasoulian

Orcid: 0009-0004-9077-521X

According to our database1, Amirhossein Rasoulian authored at least 10 papers between 2022 and 2025.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2025
Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR Angiography via Multi-Task UNet with Vesselness Prior.
Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2025, 2025

2024
Architecture Analysis and Benchmarking of 3D U-Shaped Deep Learning Models for Thoracic Anatomical Segmentation.
IEEE Access, 2024

Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Model.
Proceedings of the Image Analysis in Stroke Diagnosis and Interventions, 2024

2023
Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR.
CoRR, 2023

Weakly supervised segmentation of intracranial aneurysms using a 3D focal modulation UNet.
CoRR, 2023

Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning.
CoRR, 2023

Uncertainty-aware transformer model for anatomical landmark detection in paraspinal muscle MRIs.
Proceedings of the Medical Imaging 2023: Image Processing, 2023

FocalErrorNet: Uncertainty-Aware Focal Modulation Network for Inter-modal Registration Error Estimation in Ultrasound-Guided Neurosurgery.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Towards Multi-modal Anatomical Landmark Detection for Ultrasound-Guided Brain Tumor Resection with Contrastive Learning.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022
Weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of Attention Maps from a Swin Transformer.
Proceedings of the Machine Learning in Clinical Neuroimaging - 5th International Workshop, 2022


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