Training Hacks and a Frugal Man's Net with Application to Glioblastoma Segmentation

Jawher Ben Abdallah, Linda Marrakchi-Kacem, Islem Rekik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we investigate the effectiveness of training a sparse Neural Network on a limited number of samples in the context of brain tumor segmentation. Nowadays, Deep Learning architectures are getting deeper, more sophisticated and environmentally unfriendly in an effort to improve their segmentation performance. We use a brain tumor segmentation dataset and apply simple practices to reduce the needed computational resources to allow cheap and fast training. We also present a lighter, cheaper version of the U-Net dubbed Frugal U-Net stemming from our investigation on how far we can push the original U-Net by decreasing its parameter count using Depth-Wise Separable Convolutions instead of regular ones, all the while preserving the minimum levels of accuracy required in Medical Imaging. Our methodology is useful in clinical facilities where high-computation resources are limited.

Original languageEnglish
Title of host publicationInternational Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781665451161
DOIs
Publication statusPublished - 28 Jun 2022
Event6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022 - Sfax, Tunisia
Duration: 24 May 202227 May 2022

Publication series

NameInternational Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

Conference

Conference6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022
Country/TerritoryTunisia
CitySfax
Period24/05/2227/05/22

Keywords

  • Brain Tumour
  • Low-Budget
  • Multiclass-Segmentation
  • U-Net

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Radiology Nuclear Medicine and imaging

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