Noel C. Codella  Noel C. Codella photo         

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Research Staff Member, IBM Research AI, Computer Vision
Thomas J. Watson Research Center, Yorktown Heights, NY USA


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Professional Associations:  ACM  |  IEEE

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More information:  Melanoma Diagnostic Support Service


Dr. Codella's expertise includes Computer Science, Machine Learning, Computer Vision, Medical Imaging, Biomedical Engineering, Biology, and Physiology. In graduate school at Cornell University, Dr. Codella earned his M.Eng. degree in Computer Science, and his Ph.D. in Physiology and Medical Imaging. His thesis centered on the application of computer science and theoretical physics to Magnetic Resonance Imaging (MRI) pulse sequence design, parallel image reconstruction, and image analysis techniques for the quantification of cardiac ventricular function. His work was among the first to pioneer the use of methods to account for “partial-volume” effects in cardiac MRI image data, leading to significant improvements in the accuracy of ventricular volume quantification (producing over 200 citations). Dr. Codella additionally laid the groundwork C++ code architecture for a cardiac electrophysiology modeling project.

Since his time in graduate school, Dr. Codella has worked at the IBM T.J. Watson Research Center, with a focus towards large-scale machine learning for visual recognition. He pioneered the first study using cardiac anatomy as a biometric. He has numerous publications in machine learning applied to video event detection, satellite image retrieval, medical image retrieval and analysis, leading to over an additional 100 citations.

Most recently his work has involved dermatological and histopathology image analysis. Dr. Codella has been a co-organizer of the International Skin Imaging Collaboration (ISIC) Challenge on Melanoma Detection in 2016 and 2017 at the International Symposium on Biomedical Imaging (ISBI) Conference. The challenges have received over 120 submissions over two years. In addition, Dr. Codella's work was the first to demonstrate an artificial intelligence system capable of diagnosing melanoma from dermoscopy images with an accuracy higher than the average expert dermatologist.

Computer Science, Machine Learning, Distributed Systems, Image Analysis, Image Reconstruction, Image Acquisition, Signal Processing, Radiology (MRI/CT), Medical Imaging