1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
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…
Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks
Sara Ketabi, Dhanesh Ramachandram
Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health r…
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…
Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology
Pranav Agnihotri, Sara Ketabi, Khashayar +2
Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists…
Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators
Akino Watanabe, Sara Ketabi, Khashayar +2
As deep learning is widely used in the radiology field, the explainability of such models is increasingly becoming essential to gain clinicians' trust when using the models for dia…