Agamdeep Chopra
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View article: An Ensemble Approach for Brain Tumor Segmentation and Synthesis
An Ensemble Approach for Brain Tumor Segmentation and Synthesis Open
The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, whic…
View article: Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models Open
Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep learning architectures for tumor segmentation over the yea…
View article: An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation Open
Tumor segmentation from multi-modal brain MRI images is a challenging task due to the limited samples, high variance in shapes and uneven distribution of tumor morphology. The performance of automated medical image segmentation has been si…
View article: 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors
3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors Open
Deformable image registration is a key task in medical image analysis. The Brain Tumor Sequence Registration challenge (BraTS-Reg) aims at establishing correspondences between pre-operative and follow-up scans of the same patient diagnosed…