5 papers
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…
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…
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,…
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…