activity
20242026
most citedRadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering

27 citations · 29 across the 13 of their papers we have counts for

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Showing 2025Show all

8 papers · 1 filter

cs.CV2025

Effect of Reporting Mode and Clinical Experience on Radiologists' Gaze and Image Analysis Behavior in Chest Radiography

Mahta Khoobi, Marc Sebastian von der Stueck, Felix Barajas Ordonez +11

Structured reporting (SR) and artificial intelligence (AI) may transform how radiologists interact with imaging studies. This prospective study (July to December 2024) evaluated th…

cs.AI2025

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography

Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch +4

Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image clas…

cs.CV2025

Hallucination Filtering in Radiology Vision-Language Models Using Discrete Semantic Entropy

Patrick Wienholt, Sophie Caselitz, Robert Siepmann +6

To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs)…

cs.CV2025

Resolution scaling governs DINOv3 transfer performance in chest radiograph classification

Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl +3

Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anc…

cs.CV2025★ 2 cited

MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification

Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather +2

Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance. We present MedicalPatchNet, an inher…

physics.med-ph2025

T2 Radiomic Features Are More Sensitive Than Mean T2 for Cartilage Load Response: A Stress MRI Study

Teresa Lemainque, Philipp Schad, Simon Westfechtel +6

Objective: To assess response-to-loading in a human cadaveric knee joint model under different loading conditions before and after meniscectomy Design: In this prospective study, s…

eess.IV2025

Diagnostic Accuracy of Open-Source Vision-Language Models on Diverse Medical Imaging Tasks

Gustav Müller-Franzes, Debora Jutz, Jakob Nikolas Kather +3

This retrospective study evaluated five VLMs (Qwen2.5, Phi-4, Gemma3, Llama3.2, and Mistral3.1) using the MedFMC dataset. This dataset includes 22,349 images from 7,461 patients en…

eess.IV2025

A European Multi-Center Breast Cancer MRI Dataset

Gustav Müller-Franzes, Lorena Escudero Sánchez, Nicholas Payne +18

Early detection of breast cancer is critical for improving patient outcomes. While mammography remains the primary screening modality, magnetic resonance imaging (MRI) is increasin…