Hierarchical mix-pooling and its applications to biomedical image classification

Siyamalan Manivannan (Lead / Corresponding author), Ruixuan Wang, Emanuele Trucco

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Abstract

This paper introduces Hierarchical Mix-pooling (HMP), a translation-invariant image representation improving the discriminative power of pooling representations by capturing intermediate-size structure information in images. HMP consists of two levels, one traditional pooling (e.g., sum pooling) applied to intermediate-size regions to collect the statistics of local features, and one different pooling (e.g., max pooling) collecting statistics of the previously region-based pooled results. Classification experiments show that HMP considerably improves accuracies with much smaller sizes of dictionaries compared to traditional pooling. The superior performance of HMP is confirmed by experiments with different local features and classifiers on two public biomedical datasets (ICPR HEp-2 cells and IRMA radiology).

Original languageEnglish
Title of host publication2016 IEEE 13th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE
Pages541-544
Number of pages4
Volume2016-June
ISBN (Electronic)9781479923496
ISBN (Print)9781479923502
DOIs
Publication statusPublished - 16 Jun 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: 13 Apr 201616 Apr 2016

Conference

Conference2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
CountryCzech Republic
CityPrague
Period13/04/1616/04/16

Keywords

  • Dictionaries
  • Feature extraction
  • Biomedical imaging
  • Image coding
  • Encoding
  • Image representation
  • Support vector machines

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  • Cite this

    Manivannan, S., Wang, R., & Trucco, E. (2016). Hierarchical mix-pooling and its applications to biomedical image classification. In 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (Vol. 2016-June, pp. 541-544). [7493326] IEEE. https://doi.org/10.1109/ISBI.2016.7493326