Neural network pruning for biomedical image segmentation

Taehee Jeong; Manasa Bollavaram; Elliott Delaye; Ashish Sirasao

doi:10.1117/12.2579256

15 February 2021 Neural network pruning for biomedical image segmentation

Taehee Jeong, Manasa Bollavaram, Elliott Delaye, Ashish Sirasao

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115981J (2021) https://doi.org/10.1117/12.2579256
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Instead of manual segmentation, Segmentation using Artificial Neural Networks is very useful in biomedical image analysis. However, deploying artificial neural networks requires large memory footprint and computational costs. In this work, we propose a pruning approach to alleviate these requirements for the U-Net, which is the most popular segmentation neural network for biomedical image. Our approach handles upsampling layers and skip connections, which are essential components in U-Net architecture. We show that our approach achieves 2x speedup, more than 7x size reduction with less than 2% loss in average intersection-over-union (IOU) on PhC- U373 and DIC-HeLa biomedical data set.

Conference Presentation

Citation Download Citation

Taehee Jeong, Manasa Bollavaram, Elliott Delaye, and Ashish Sirasao "Neural network pruning for biomedical image segmentation", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115981J (15 February 2021); https://doi.org/10.1117/12.2579256

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