A Multiresolution Deep Learning Framework for Automated Annotation of Reflectance Confocal Microscopy Images
Apr 6, 2018·
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0 min read
Kivanc Kose
Alican Bozkurt
Christi Alessi-Fox
Melissa Gill
Dana H. Brooks
Jennifer Dy
Milind Rajadhyaksha
Abstract
Morphological tissue patterns in RCM images are critical in diagnosis of melanocytic lesions. We present a multiresolution deep learning framework that can automatically annotate RCM images for these diagnostic patterns with high sensitivity and specificity.
Type
Publication
In Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS)
Authors
AI Scientist
I am an AI Scientist at Paige AI. I did my Ph.D. with Jennifer Dy, Dana Brooks, and Jan-Willem van de Meent at Northeastern University. My main research interests are machine learning with emphasis on probabilistic programming, deep neural networks, and their applications in biomedical image processing. I am one of the developers of Probabilistic Torch, a library for deep generative models that extends PyTorch. I am also one of the maintainers of the PyTorch distributions module.