PocketNet: A Smaller Neural Network for Medical Image Analysis

Authors

Adrian Celaya
Jonas A Actor
Rajarajesawari Muthusivarajan
Evan Gates
Caroline Chung
Dawid Schellingerhout
Beatrice Riviere
David Fuentes

Publication Date

4-1-2023

Journal

IEEE Transactions on Medical Imaging

Abstract

Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNet paradigm to reduce the size of deep learning models by throttling the growth of the number of channels in convolutional neural networks. We demonstrate that, for a range of segmentation and classification tasks, PocketNet architectures produce results comparable to that of conventional neural networks while reducing the number of parameters by multiple orders of magnitude, using up to 90% less GPU memory, and speeding up training times by up to 40%, thereby allowing such models to be trained and deployed in resource-constrained settings.

Keywords

Neural network, segmentation, pattern recognition and classification

Comments

PMID: 36427285