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A Novel Multispace Image Reconstruction Method for Pathological Image Classification Based on Structural Information.

Pathological image classification is of great importance in various biomedical applications, such as for lesion detection, cancer subtype identification, and pathological grading. To this end, this paper proposed a novel classification framework using the multispace image reconstruction inputs and t...

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Published in:BioMed Research International pp. 1 - 10
Main Authors: Zhu, Honglin, Jiang, Huiyan, Li, Siqi, Li, Haoming, Pei, Yan
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Wiley-Blackwell 4/11/2019
Online Access:View this record in EBSCOhost
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      jtl: BioMed Research International
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      dt: 4/11/2019
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        135834782
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        135834782
        10.1155/2019/3530903
        135834782
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        atl: A Novel Multispace Image Reconstruction Method for Pathological Image Classification Based on Structural Information.
      aug:
        au:
          Zhu, Honglin
          Jiang, Huiyan
          Li, Siqi
          Li, Haoming
          Pei, Yan
        affil: Department of Software College, Northeastern University, Shenyang 110819, China
      sug:
        subj:
          Image Processing, Computer Assisted
          Machine Learning
          Pathology, Clinical Classification
          Human
          Semantics
          Microscopy
          Lymphoma Diagnosis
      ab: Pathological image classification is of great importance in various biomedical applications, such as for lesion detection, cancer subtype identification, and pathological grading. To this end, this paper proposed a novel classification framework using the multispace image reconstruction inputs and the transfer learning technology. Specifically, a multispace image reconstruction method was first developed to generate a new image containing three channels composed of gradient, gray level cooccurrence matrix (GLCM) and local binary pattern (LBP) spaces, respectively. Then, the pretrained VGG-16 net was utilized to extract the high-level semantic features of original images (RGB) and reconstructed images. Subsequently, the long short-term memory (LSTM) layer was used for feature selection and refinement while increasing its discrimination capability. Finally, the classification task was performed via the softmax classifier. Our framework was evaluated on a publicly available microscopy image dataset of IICBU malignant lymphoma. Experimental results demonstrated the performance advantages of our proposed classification framework by comparing with the related works.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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