1 citations · 1 across the 10 of their papers we have counts for
10 papers
Automated Chest CT Protocol Selection via Large Language Model Derived Text Embeddings from Imaging Request Text
Zahra Hosseini, Mahan Pouromidi, Farzad Khalvati +1
Purpose: Accurate CT protocol selection is critical for diagnostic quality and patient safety, yet the current process is manual, time-consuming, and prone to inconsistencies. Prio…
Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins +3
Multimodal Contrastive Learning (CL) has shown significant performance in aligning representations across various data modalities and improving downstream tasks, especially in heal…
Exploring Radiologists' Expectations of Explainable Machine Learning Models in Medical Image Analysis
Sara Ketabi, Matthias W. Wagner, Birgit Betina Ertl-Wagner +2
In spite of the strong performance of machine learning (ML) models in radiology, they have not been widely accepted by radiologists, limiting clinical integration. A key reason is…
Artificial Intelligence for Sentiment Analysis of Persian Poetry
Arash Zargar, Abolfazl Moshiri, Mitra Shafaei +3
Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textu…
ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modeling
Martin Licht, Sara Ketabi, Farzad Khalvati
Topic modeling is a useful tool for analyzing large corpora of written documents, particularly academic papers. Despite a wide variety of proposed topic modeling techniques, these…
A Comparison and Evaluation of Fine-tuned Convolutional Neural Networks to Large Language Models for Image Classification and Segmentation of Brain Tumors on MRI
Felicia Liu, Jay J. Yoo, Farzad Khalvati
Large Language Models (LLMs) have shown strong performance in text-based healthcare tasks. However, their utility in image-based applications remains unexplored. We investigate the…
ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion
Meng Zhou, Farzad Khalvati
Multimodal medical image fusion integrates complementary information from different imaging modalities to enhance diagnostic accuracy and treatment planning. While deep learning me…
AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
Connor Ludwig, Khashayar Namdar, Farzad Khalvati
Medical image segmentation has greatly aided medical diagnosis, with U-Net based architectures and nnU-Net providing state-of-the-art performance. There have been numerous general…
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis
Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins +3
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workfl…
Opportunities for Persian Digital Humanities Research with Artificial Intelligence Language Models; Case Study: Forough Farrokhzad
Arash Rasti Meymandi, Zahra Hosseini, Sina Davari +6
This study explores the integration of advanced Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques to analyze and interpret Persian literature, focusing…