Lightweight Deep Network for the Classification of Breast Cancer Histopathological Images

Muhammad Waqas*, Tomas Maul, Iman Yi Liao, Amr Ahmed

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

Abstract

Out of all cancers among female patients, breast cancer is on top in terms of prevalence. Many deep learning approaches have been investigated for the classification of histopathological images of breast cancer, however most tend to be either too complex or too large to adopt at a clinical level. In this study we propose a lightweight network, based on knowledge distillation, which performs almost the same as the teacher network but with significantly fewer parameters. Our student network consists of inverted residual blocks of MobileNetV2 and the ghost module. We performed our experiments on BreakHis, BACH and Kaggle breast cancer histopathological imaging datasets. The results show that different versions of our proposed lightweight student architecture perform with similar accuracy levels compared with the teacher network, while using significantly fewer parameters.

Original languageEnglish
Title of host publicationProceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
EditorsXin Chen, Lin Cao, Qingli Li, Yan Wang, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665488877
DOIs
Publication statusPublished - 21 Dec 2022
Event15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022 - Beijing, China
Duration: 5 Nov 20227 Nov 2022

Publication series

NameProceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022

Conference

Conference15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
Country/TerritoryChina
CityBeijing
Period5/11/227/11/22

Keywords

  • Breast cancer
  • Histopathological Images
  • Knowledge Distillation

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