Search Results for author:
Found 6 papers, 3 papers with code
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports
While recent publications have explored GPT-4 in its application to extracting information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to different leading open-source models.
3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology
The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged.
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query
We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions.
CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI
We use the CheXpert and PadChest public datasets to build and test a medical imaging AI drift monitoring workflow to track data and model drift without contemporaneous ground truth.
DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head Images
Thus, DeepRadiologyNet enables significant reduction in the workload of human radiologists by automatically filtering studies and reporting on the high-confidence ones at an operating point well below the literal error rate for US Board Certified radiologists, estimated at 0. 82%.
Dense Volume-to-Volume Vascular Boundary Detection
In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data.
