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: 

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.

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

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Leticia Ortiz | Marketing & Communications | Building a community around data science in biomedicine​