2 citations · 2 across the 7 of their papers we have counts for
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Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
Tianyi Ren, Juampablo Heras Rivera, Hitender Oswal +4
Deep learning has been successfully applied to medical image segmentation, enabling accurate identification of regions of interest such as organs and lesions. This approach works e…
An Ensemble Approach for Brain Tumor Segmentation and Synthesis
Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren +14
The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy,…
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation
Tianyi Ren, Ethan Honey, Harshitha Rebala +3
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 perf…
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models
Tianyi Ren, Abhishek Sharma, Juampablo Heras Rivera +5
Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep…
3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors
Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra +1
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-…