78 citations · 164 across the 5 of their papers we have counts for
5 papers
Comprehensive language-image pre-training for 3D medical image understanding
Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao +13
In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abn…
MAIRA-2: Grounded Radiology Report Generation
Shruthi Bannur, Kenza Bouzid, Daniel C. Castro +18
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solut…
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Anja Thieme, Abhijith Rajamohan, Benjamin Cooper +22
Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious h…
Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Maria T. Wetscherek +18
Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare a…
Exploring scalable medical image encoders beyond text supervision
Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor +12
Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal sys…