From the 1 of 5 linked papers with an AI index.
5 papers
Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging
Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins +3
The paper proposes a multimodal semantic-aware contrastive learning framework that uses semantic similarity from radiology reports to reduce false negatives when training on 3D bra…
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…