Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Valentina Pedoia

Valentina Pedoia
Data Scientist and Radiology Specialist, Radiology and Biomedical Imaging and Center of Digital Health Innovation, UCSF


Valentina Pedoia is a specialist in the Musculoskeletal and Imaging Research Group at UCSF and a data scientist focusing on developing algorithms for advanced computer vision and machine learning for improving the usage of noninvasive imaging as diagnostic and prognostic tools. Her current research explores the role of machine learning in the extraction of contributors to osteoarthritis (OA), and she is studying analytics to model the complex interactions between morphological, biochemical, and biomechanical aspects of the knee joint as a whole and deep learning convolutional neural network for musculoskeletal tissue segmentation and for the extraction of silent features from quantitative relaxation maps for a comprehensive study of the biochemical articular cartilage composition with the ultimate goal of developing a completely data-driven model that is able to extract imaging features and use them to identify risk factors and predict outcomes. Previously, she was a postdoc in the Musculoskeletal and Imaging Research Group, where she provided support and expertise in medical computer vision with a focus on reducing human effort and extracting semantic features from MRIs to study degenerative joint disease. Valentina’s recent work on machine learning applied to OA was awarded as annual scientific highlights of the 25th conference of the International Society of Magnetic Resonance In Medicine (ISMRM 2017) and selected as best paper presented at the MRI drug discovery study group. Valentina holds a PhD in computer science, where her research focused on feature extraction from functional and structural brain MRI in subjects with glial tumors.


11:50am12:30pm Thursday, March 8, 2018
Jiao(Jennie) Wang (Intel), Valentina Pedoia (UCSF), Berk Norman (UCSF), Yulia Tell (Intel)
Damage to the meniscus is a physically limiting injury that can lead to further medical complications. Automatically classifying this damage at the time of an MRI scan would allow quicker and more accurate diagnosis. Jennie Wang, Valentina Pedoia, Berk Norman, and Yulia Tell offer an overview of their classification system built with 3D convolutional neural networks using BigDL on Apache Spark. Read more.