Research & Education

london

UCLA Radiological Sciences @ ISMRM 2022
04/28/2022

For the complete list of UCLA Radiological Sciences faculty participating in the 2022 ISMRM annual meeting, please visit the ISMRM 2022 roster.

annual meeting

UCLA Radiological Sciences @ ISMRM 2021
05/14/2021

For the complete list of UCLA Radiological Sciences faculty participating in the 2021 ISMRM annual meeting, please visit the ISMRM 2021 roster.

one community

UCLA Radiological Sciences @ ISMRM 2020
08/05/2020

For the complete list of UCLA Radiological Sciences faculty participating in the 2020 ISMRM annual meeting, please visit the ISMRM 2020 roster.

RSNA

UCLA Radiological Sciences at RSNA 2019
11/08/2019

For complete information please visit the RSNA 2019 roster.

FocalNet

FocalNet: Artificial Intelligence Identifies Prostate Cancer
05/08/2019

UCLA Radiology researchers, led by Ruiming Cao, Kyung Sung, PhD and Steven Raman, MD, have developed a new artificial intelligence application — FocalNet — that detects prostate cancer lesions and predicts their aggressiveness on multi-parametric magnetic resonance imaging (mp-MRI) scans. With comparisons to the prospective performance of radiologists using the current diagnostic guideline, FocalNet demonstrated nearly the same level of accuracy as radiologists with 10 years of clinical prostate MRI reading experience.

Authors Ruiming Cao, Dr. Steven Raman and Kyung Sung.
Authors Ruiming Cao, Dr. Steven Raman and Kyung Sung.

FocalNet is a novel multi-class convolutional neural network (CNN) that automatically learns to assess and classify prostate lesions in a consistent way. The system learned by analyzing mp-MRI scans of 417 men with prostate cancer and comparing its own results to the actual pathology specimen.

The researchers compared FocalNet with the prospective clinical performance of three highly-experienced genitourinary radiologists for lesion detection and did not find statistically significant differences. As prostate MRI reading quality largely varies according to the radiologist’s experience, FocalNet can potentially assist both experienced and less experienced radiologists or augment the prostate cancer detection task for non-experts to minimize diagnostic errors on MRI.

The research is published in the IEEE Transactions on Medical Imaging: “Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet.” The paper was presented at the IEEE International Symposium on Biomedical Imaging in April 2019 and was selected as the runner up-for best paper.