Evaluations of Deep Learning Methods for Pathology Image Classification

Sheng Kai Huang, Cai Rong Yu, Yi Sheng Liao, Chun Rong Huang

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

7 Citations (Scopus)

Abstract

While the state-of-the-art deep neural networks have been shown their effectiveness to achieve the general image classification tasks, the performance evaluations of these networks on the pathology image classification have not been well discussed. In this paper, we aim to evaluate the state-of-the-art deep learning methods including the convolutional neural network, deep ensemble network and transformers on several public pathology datasets. By comparing these methods, we empirically find that the transformers and deep ensemble network serve as good backbone networks for pathology image classification.

Original languageEnglish
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationIntelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages95-99
Number of pages5
ISBN (Electronic)9781665469173
DOIs
Publication statusPublished - 2022
Event2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan
Duration: 2022 Oct 132022 Oct 15

Publication series

NameBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

Conference

Conference2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/TerritoryTaiwan
CityTaipei
Period22-10-1322-10-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Neuroscience (miscellaneous)
  • Instrumentation

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