Cervical Histopathology Image Classification Using Multilayer Hidden Conditional Random Fields and Weakly Supervised Learning

In this paper, a novel
multilayer hidden conditional random fields
(MHCRFs)-based
cervical histopathology image classification
(CHIC) model is proposed to classify well, moderate and poorly differentiation stages of cervical cancer using a
weakly supervised learning
strategy. First, the color, texture, and
deep learning
features are extracted to represent the histopathological image patches. Then, based on the extracted features,
artificial neural network
,
support vector machine
, and
random forest
classifiers are designed to calculate the patch-level classification probabilities. Third, effective classifiers are selected to generate unary and binary potentials. At last, using the generated potentials, the final image-level classification results are predicted by our MHCRF model, and an overall accuracy around 77.32% is obtained on six practical cervical histopathological image datasets with more than 600 immunohistochemical (IHC) stained samples. Among the six test accuracies, the highest reaches 88%. Furthermore, we also test our MHCRF method with a gastric hematoxylin-eosin (HE) stained histopathological image dataset including 200 images for an extended experiment, and achieve an accuracy of 93%.