12 citations · 12 across the 3 of their papers we have counts for
3 papers
UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
Shiv Ghosh, Junayd Lateef, Chih-Hua Liu +3
Brain tumor diagnosis is largely dependent on Magnetic Resonance Imaging (MRI) evaluation, which requires radiologists to synthesize thousands of images across multiple 3D sequence…
Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRI
Arvind Murari Vepa, Yannan Yu, Jingru Gan +6
Multiparametric 3D brain MRI (mpMRI) is central to neuroradiology, but producing tumor location, appearance, size, and involvement of critical structures for neurosurgical planning…
Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT
Sophie Ostmeier, Brian Axelrod, Benjamin Pulli +6
Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Con…