A deep residual inception network for HEp-2 cell classification

Yuexiang Li, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

Indirect-immunofluorescence (IIF) of Human Epithelial-2 (HEp-2) cells is a commonly-used method for the diagnosis of autoimmune diseases. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. In this paper, we proposed a hybrid deep learning network combining the latest high-performance network architectures, i.e. ResNet and Inception, to automatically classify HEp-2 cell images. The proposed Deep Residual Inception (DRI) net replaces the plain convolutional layers in Inception with residual modules for better network optimization and fuses the features extracted from shallow, medium and deep layers for performance improvement. The proposed model is evaluated on publicly available I3A (Indirect Immunofluorescence Image Analysis) dataset. The experiment results demonstrate that our proposed DRI remarkably outperforms the benchmarking approaches.

Original languageEnglish
Title of host publicationDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings
EditorsTal Arbel, M. Jorge Cardoso
PublisherSpringer Verlag
Pages12-20
Number of pages9
ISBN (Print)9783319675572
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 14 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10553 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period14/09/1714/09/17

Keywords

  • Deep learning network
  • HEp-2 cells
  • Image classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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