Fine-grained Colon Pathology Images Classification Based on Heterogeneous Ensemble Learning with Multi-distance Measures
Fine-grained classification of colon pathology images is of great significance for both symptomatic treatment and prog-nosis assessment.However,the histopathological subtyping images of colon are extremely similar in morphology.It is a challeng-ing 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 la-bels 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.
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