2 citations · 2 across the 12 of their papers we have counts for
12 papers
Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
Agamdeep Chopra, Mehmet Kurt
Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition framework th…
CoRe-BT: A Multimodal Radiology-Pathology-Text Benchmark for Robust Brain Tumor Typing
Juampablo E. Heras Rivera, Daniel K. Low, Xavier Xiong +5
Accurate brain tumor typing requires integrating heterogeneous clinical evidence, including magnetic resonance imaging (MRI), histopathology, and pathology reports, which are often…
BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features
Juampablo E. Heras Rivera, Dickson T. Chen, Tianyi Ren +4
Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology has been limited due to a lack of ope…
SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis
Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren +3
Tau positron emission tomography supports Alzheimer's disease staging but is difficult to scale because of tracer, scanner, and radiation constraints. Synthesis from structural MRI…
Clinical Interpretability of Deep Learning Segmentation Through Shapley-Derived Agreement and Uncertainty Metrics
Tianyi Ren, Daniel Low, Pittra Jaengprajak +3
Segmentation is the identification of anatomical regions of interest, such as organs, tissue, and lesions, serving as a fundamental task in computer-aided diagnosis in medical imag…
Real-time nonlinear inversion of magnetic resonance elastography with operator learning
Juampablo E. Heras Rivera, Caitlin M. Neher, Mehmet Kurt
To develop and evaluate an operator learning framework for nonlinear inversion (NLI) of brain magnetic resonance elastography (MRE) data, which enables real-tim…
Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings
Juampablo E. Heras Rivera, Hitender Oswal, Tianyi Ren +4
Stroke is among the top three causes of death worldwide, and accurate identification of ischemic stroke lesion boundaries from imaging is critical for diagnosis and treatment. The…
How We Won the ISLES'24 Challenge by Preprocessing
Tianyi Ren, Juampablo E. Heras Rivera, Hitender Oswal +4
Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treatment. Supervised deep learning m…
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…