Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230400043-7.doi: 10.11896/jsjkx.230400043

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Fine-grained Colon Pathology Images Classification Based on Heterogeneous Ensemble Learningwith Multi-distance Measures

LIANG Meiyan1, FAN Yingying1, WANG Lin2,3   

  1. 1 Shanxi University School of Physics and Electronic Engineering,Taiyuan 030006,China
    2 Shanxi Bethune Hospital,Shanxi Academy of Medical Sciences,Tongji Shanxi Hospital,Third Hospital of Shanxi Medical University,Taiyuan 030032,China
    3 Tongji Hospital Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China
  • Published:2024-06-06
  • About author:LIANG Meiyan,born in 1984,Ph.D,associate professor.Her main research interests include machine learning,deep learning and medical image processing.
  • Supported by:
    National Natural Science Foundation of China(11804209),Natural Science Foundation of Shanxi Province,China(201901D211173,202103021223411) and Shanxi Higher Education Institution Science and TechnologyInnovation Grants Program,China(2019L0064).

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Abstract: Fine-grained classification of colon pathology images is of great significance for both symptomatic treatment and prognosis assessment.However,the histopathological subtyping images of colon are extremely similar in morphology.It is a challenging task for manual methods to obtain high-precision predictions.Computer-aided diagnosis methods based on a single model also suffer from predictive bias in histological subtyping.Therefore,the fine-grained classification algorithm based on heterogeneous ensemble learning with multi-distance measures is proposed to predict the microsatellite state of colon pathology images.This method ensembles the predictions of the base learners by measuring the distance between the output confidence scores and the labels in latent space using Cosine distance,Manhattan distance,and Euclidean distance,respectively.Then,these distances are used to improve the overall decision performance of the model.The results show that the classification accuracy,precision,recall and F-1 score can reach 94% in the fine-grained classification,which provides a new perspective for subtype classification of pathological images.

Key words: Ensemble learning, Fine-grained classification, Distance measure, Pathology images, Microsatellite subtyping

CLC Number: 

  • TP391
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