18 citations · 55 across the 15 of their papers we have counts for
15 papers
UQMIA: An Open, Hands-On Tutorial on Uncertainty Quantification in Medical Imaging Analysis with Large Language Model-Based Assessment of Educational Content
Benyamin Gheiji, Danial Elyassirad, Mahsa Vatanparast +2
Uncertainty quantification (UQ) is increasingly recognized as an important component of reliable machine learning in medical imaging, yet practical resources connecting UQ theory,…
MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination
Saisha Shetty, Satvik Tripathi, Austin Lin +6
We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent orchestration for clinical…
SPARC-Rad: A Multimodal Benchmark Dataset and Evaluation Pipeline for Spatial and Anatomical Reasoning in Radiology Vision-Language Models
Satvik Tripathi, Mustafa Ege Seker, Kristian Quevada +8
Vision-language models (VLMs) are increasingly being evaluated for medical imaging, but many available benchmarks emphasize disease classification, report generation, or broad visu…
CONRep: Uncertainty-Aware Vision-Language Report Drafting Using Conformal Prediction
Danial Elyassirad, Benyamin Gheiji, Mahsa Vatanparast +5
Automated radiology report drafting (ARRD) using vision-language models (VLMs) has advanced rapidly, yet most systems lack explicit uncertainty estimates, limiting trust and safe c…
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
Nazanin Maleki, Raisa Amiruddin, Ahmed W. Moawad +240
Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis.…
CONSeg: Voxelwise Glioma Conformal Segmentation
Danial Elyassirad, Benyamin Gheiji, Mahsa Vatanparast +2
Background and Purpose: Glioma segmentation is crucial for clinical decisions and treatment planning. Uncertainty quantification methods, including conformal prediction (CP), can e…
Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients
Danial Elyassirad, Benyamin Gheiji, Mahsa Vatanparast +5
Gliomas are the most common cause of mortality among primary brain tumors. Molecular markers, including Isocitrate Dehydrogenase (IDH) and O[6]-methylguanine-DNA methyltransferase…
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Maria Correia de Verdier, Rachit Saluja, Louis Gagnon +82
Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the gene…
BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions
Spyridon Bakas, Siddhesh P. Thakur, Shahriar Faghani +19
Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is wide…
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Dominic LaBella, Valeriia Abramova, Mehdi Astaraki +102
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional da…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
RadRotator: 3D Rotation of Radiographs with Diffusion Models
Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani +4
Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few…
CONFLARE: CONFormal LArge language model REtrieval
Pouria Rouzrokh, Shahriar Faghani, Cooper U. Gamble +2
Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for g…
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection
Cooper Gamble, Shahriar Faghani, Bradley J. Erickson
As deep learning (DL) continues to demonstrate its ability in radiological tasks, it is critical that we optimize clinical DL solutions to include safety. One of the principal conc…
Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani +3
Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the perfor…