Weakly Supervised Cervical Histopathological Image Classification Using Multilayer Hidden Conditional Random Fields

In this paper, a novel Multilayer Hidden Conditional Random Fields based weakly supervised Cervical Histopathological Image Classification framework is proposed to classify well, moderately and poorly differentiation stages of cervical cancer. First, 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 probability. Thirdly, effective features are selected to generate unary and binary potentials of the proposed Multilayer Hidden Conditional Random Fields framework. Lastly, using the generated potentials, the final image-level classification result is predicted by our Multilayer Hidden Conditional Random Fields model, and an accuracy of 88% is obtained on a practical histopathological image dataset with more than 100 AQP stained samples.