Histopathological image recognition with discriminant-oriented extreme learning machine autoencoder based deep feature dimension reduction

Histopathological image recognition with discriminant-oriented extreme learning machine autoencoder based deep feature dimension reduction

연구 분야: Artificial Intelligence

학회: Signal, Image and Video Processing

Deep learning can extract deep sematic features of histopathological images, which plays an important role in machine learning based disease diagnosis. However, with the improvement of the network level, the extracted image features increase dramatically. To avoid the curse of dimensionality, this work proposes a deep feature dimension reduction method, i.e. discriminant-oriented extreme learning machine autoencoder (DELM-AE), for the histopathological image recognition task. Considering the high computational efficiency of the ELM-AE algorithm, but with inherent limitations in classification tasks, the penalty terms to enhance sample discriminability are added to the objective function of DELM-AE, and then a fast algorithm for determining the projection coefficient matrix is presented. Through comparison experiments on dimensionality reduction representation and recognition of histopathology image deep features extracted based on ResNet50, it was verified that the DELM-AE based method can improve the effectiveness and generalization ability of ML classifiers while reducing feature dimensions on two datasets, demonstrating the potential of its feature representation for classification tasks.