Congratulations to Dr. Eran Halperin and his team at University of California, Los Angeles for their recently published Nature article that proposes a new deep learning technique (SLIVER-net) to predict clinical features from 3-dimensional volumes using a limited number of manually annotated examples. The open source, peer reviewed article “Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging publication” can be found here:

https://lnkd.in/eS_ZHx6 

The study is a collacoration of Computational Medicine Department and the Doheny Eye Institute at UCLA, led by Eran Halperin & SriniVas R. Sadda

SLIVER-net predicts risk factors for progression of age-related macular degeneration (AMD), a leading cause of blindness, from optical coherence tomography (OCT) volumes acquired from multiple sites. SLIVER-net successfully predicts these factors despite being trained with a relatively small number of annotated volumes (hundreds) and only dozens of positive training examples. Our empirical evaluation demonstrates that SLIVER-net significantly outperforms standard state-of-the-art deep learning techniques used for medical volumes, and its performance is generalizable as it was validated on an external testing set. In a direct comparison with a clinician panel, we find that SLIVER-net also outperforms junior specialists, and identifies AMD progression risk factors similarly to expert retina specialists.

Authors:
Nadav Rakocz, Jeffrey N. Chiang, Muneeswar G. Nittala, Giulia Corradetti, Liran Tiosano, Swetha Velaga, Michael Thompson, Brian L. Hill, Sriram Sankararaman, Jonathan L. Haines, Margaret A. Pericak-Vance, Dwight Stambolian, Srinivas R. Sadda & Eran Halperin

Media Contact: 

Leticia Ortiz | Marketing & Communications | Building a community around data science in biomedicine
leticiaortiz@mednet.ucla.edu​
@CompMedUCLA