Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images

Amirreza Mahbod*, Rahim Entezari, Isabella Ellinger, Olga Saukh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Recently, pruning deep neural networks (DNNs) has received a lot of attention for improving accuracy and generalization power, reducing network size, and increasing inference speed on specialized hardwares. Although pruning was mainly tested on computer vision tasks, its application in the context of medical image analysis has hardly been explored. This work investigates the impact of well-known pruning techniques, namely layer-wise and network-wide magnitude pruning, on the nuclei instance segmentation performance in histological images. Our utilised instance segmentation model consists of two main branches: (1) a semantic segmentation branch, and (2) a deep regression branch. We investigate the impact of weight pruning on the performance of both branches separately, and on the final nuclei instance segmentation result. Evaluated on two publicly available datasets, our results show that layer-wise pruning delivers slightly better performance than network-wide pruning for small compression ratios (CRs) while for large CRs, network-wide pruning yields superior performance. For semantic segmentation, deep regression and final instance segmentation, 93.75%, 95%, and 80% of the model weights can be pruned by layer-wise pruning with less than 2% reduction in the performance of respective models.
Original languageEnglish
Title of host publicationApplications of Medical Artificial Intelligence. AMAI 2022
Pages108 - 117
ISBN (Electronic)978-303117720-0
DOIs
Publication statusPublished - 16 Sept 2022
EventMICCAI workshop on Applications of Medical AI - Singapore, Singapore, Singapore
Duration: 18 Sept 202218 Sept 2022
https://sites.google.com/view/amai2022/home#h.ejcv6c56z2ym

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Science and Business Media Deutschland GmbH
Volume13540
ISSN (Electronic)0302-9743

Conference

ConferenceMICCAI workshop on Applications of Medical AI
Abbreviated titleAMAI
Country/TerritorySingapore
CitySingapore
Period18/09/2218/09/22
Internet address

Keywords

  • Machine Learing
  • Neural networks
  • pruning
  • segmentation
  • Medical Imaging

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