1 citations · 1 across the 3 of their papers we have counts for
4 papers
Closing the Performance Gap Between AI and Radiologists in Chest X-Ray Reporting
Harshita Sharma, Maxwell C. Reynolds, Valentina Salvatelli +26
AI-assisted report generation offers the opportunity to reduce radiologists' workload stemming from expanded screening guidelines, complex cases and workforce shortages, while main…
Data Scaling Laws for Radiology Foundation Models
Maximilian Ilse, Harshita Sharma, Anton Schwaighofer +12
Foundation vision encoders such as CLIP and DINOv2, trained on web-scale data, exhibit strong transfer performance across tasks and datasets. However, medical imaging foundation mo…
Insights into a radiology-specialised multimodal large language model with sparse autoencoders
Kenza Bouzid, Shruthi Bannur, Felix Meissen +4
Interpretability can improve the safety, transparency and trust of AI models, which is especially important in healthcare applications where decisions often carry significant conse…
MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
Harshita Sharma, Valentina Salvatelli, Shaury Srivastav +13
There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information…