Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images

Abstract

Classification of non-small-cell lung cancer (NSCLC) into adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) via histopathology is a vital prerequisite to select the appropriate treatment for lung cancer patients. Most machine learning approaches rely on manually annotating large numbers of whole slide images (WSI) for training. However, manually delineating cancer areas or even single cancer cells on hundreds or thousands of slides is tedious, subjective and requires highly trained pathologists. We propose to use Neural Image Compression (NIC), which requires only slide-level labels, to classify NSCLC into LUSC and LUAD. NIC consists of two phases/networks. In the first phase the slides are compressed with a convolutional neural network (CNN) acting as an encoder. In the second phase the compressed slides are classified with a second CNN. We trained our classification model on >2,000 NIC-compressed slides from the TCGA and TCIA databases and evaluated the model performance additionally on several internal and external cohorts. We show that NIC approaches state of the art performance on lung cancer classification, with an average AUC of 0.94 on the TCGA and TCIA testdata, and AUCs between 0.84 and 0.98 on other independent datasets.