MLLCD: A Meta Learning-based Method for Lung Cancer Diagnosis Using Histopathology Images

Lung cancer is a leading cause of death. An accurate early lung cancer diagnosis can improve a patient’s survival chances. Histopathological images are essential for cancer diagnosis. With the development of deep learning in the past decade, many scholars have used deep learning to learn the features of histopathological images and achieve lung cancer classification. However, deep learning requires a large quantity of annotated data to train the model to achieve a good classification effect, and collecting many annotated pathological images is time-consuming and expensive. Faced with the scarcity of pathological data, we present a meta-learning method for lung cancer diagnosis (called MLLCD). In detail, the MLLCD works in three steps. First, we preprocess all data using the bilinear interpolation method and then design the base learner which units a convolutional neural network(CNN) and transformer to distill local features and global features of pathology images with different resolutions. Finally, we train and update the base learner with a model-agnostic meta-learning (MAML) algorithm. Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer patient data demonstrate that our proposed model achieves the receiver operating characteristic (ROC) values of 0.94 for lung cancer diagnosis.