Deep Residual Nets For Improved Alzheimer’s Diagnosis
Document Type
Conference Proceeding
Publication Date
2017
Published In
Proceedings Of The 8th ACM Conference On Bioinformatics, Computational Biology And Health Informatics
Abstract
We propose a framework that leverages deep residual CNNs pretrained on large, non-biomedical image data sets. These pretrained networks learn cross-domain features that improve low-level interpretation of images. We evaluate our model on brain imaging data and show that pretraining and the use of deep residual networks are crucial to seeing large improvements in Alzheimer's Disease diagnosis from brain MRIs.
Published By
ACM
Conference
8th ACM Conference On Bioinformatics, Computational Biology And Health Informatics
Conference Dates
August 20-23, 2017
Conference Location
Boston, MA
Recommended Citation
Aly A. Valliani , '16 and Ameet Soni.
(2017).
"Deep Residual Nets For Improved Alzheimer’s Diagnosis".
Proceedings Of The 8th ACM Conference On Bioinformatics, Computational Biology And Health Informatics.
615-615.
DOI: 10.1145/3107411.3108224
https://works.swarthmore.edu/fac-comp-sci/45