Blog

A Natural Language Processing (NLP) Search Algorithm to Facilitate Use of Appropriateness Criteria

The latest from the Big Data in Radiology (BDRAD) research team who developed and evaluated a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations.

UCSF Researchers Use Gene Expression Data to Map Cell Types in the CNS

Prior attempts to quantify the cell-type composition of the brain have been limited in both the spatial resolution of the resulting atlas, and the number and specificity of the cell types mapped. To address this gap in knowledge, investigators co-led the development of a computational pipeline,...

Participate in UCSF ci2, MICCAI 2022 Challenge: K2S K-Space2Segmentation

Each year, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) hosts competitive challenges at their international conference. This year, Valentina Pedoia, PhD, UCSF Radiology faculty and member of the UCSF Center for Intelligent Imaging (ci2), and her team will host "...

A Step Toward Using Computer Vision to Accurately Use FAST for Injured Children

A team of investigators led by Aaron Kornblith, MD, a member of the UCSF Center for Intelligent Imaging (ci2), physician scientist and faculty member in the UCSF Department of Emergency Medicine, sought to develop an expert definition for FAST in children as the first step to improving reliability...

Using AI-Based Image Enhancement to Reduce Brain MRI Scan Times and Improve SNR

Recently, authors from UCSF Radiology and Subtle Medical prospectively evaluated AI-based image enhancement in patients undergoing clinical brain MRI.

Parul Verma Awarded An Alzheimer's Association Research Fellowship

Parul Verma, UCSF Radiology postdoctoral scholar, was recently awarded an Alzheimer's Association Research Fellowship.

Building Methods and Tools for More Efficient Curation of Image Data

As a physician-scientist and a practicing cardiologist, UCSF Center for Intelligent Imaging (ci2) member Rima Arnaout, MD studies how machine learning and artificial intelligence (AI) can reduce diagnostic errors in medical imaging and lead to new insights on cardiovascular disease.

Introducing Representational Gradient Boosting (RGB)

After three years, investigators from the UCSF Center for Intelligent Imaging (ci2) are very excited to announce that their work on Representational Gradient Boosting (RGB) has just been published!

Examining Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

Investigators from UCSF Radiology and Penn Radiology sought to figure out why and also assess the effect of multi-institutional training datasets for mitigating performance loss.

Using Bayesian Deep Learning for more Accurate and Robust PET/MRI Scanning

Hybrid PET/MRI systems combine the functional information from PET tracers with the soft tissue contrast from MRI. Here at the UCSF Center for Intelligent Imaging (ci2), Peder Larson, PhD and Thomas Hope, MD lead a team of investigators interested in developing improvements for simultaneous PET/MR...