Deep Learning-based NSCLC Classification from Whole-Slide Images: Leveraging Expectation-Maximization and InceptionV3

Abstract

The standard-of-care treatment for Non-Small Cell Lung Cancer (NSCLC) is carried out via hematoxylin and eosin (H&E)-stained whole slide tissue images (WSI). These WSIs assist in classifying NSCLC into its prominent subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC)– a process, though efective, yet cumbersome for the pathologists when performed manually. To assist medical practitioners, a robust Artificial intelligence-based model (such as convolutional neural networks (CNN)) could be utilized to classify WSIs automatically. However, training gigapixel-level WSIs directly on a CNN is computationally prohibitive. This work proposes a novel EM-Inception model for efficient classification of NSCLC WSIs via a decision fusion approach. For effective memory utilization, the WSIs are initially segmented into patches. Next, a subset of discriminative patches (high likelihood of having tumorous region) is identifed with the assistance of the InceptionV3-based model and the EM algorithm–the model training is started with all the patches of each WSI, consecutively executing the EM algorithm to retain only the discriminative patches based on the patch-level predictions of the model. Finally, the WSI-level predictions are obtained by aggregating the discriminative patch-level predictions using the voting method. Post-training, EM-Inception’s classification efficacy is evaluated using 10-fold cross-validation. The proposed model achieves a classification performance of [0.87 ± 0.03] accuracy, [0.85 ± 0.12] specificity, [0.89±0.12] sensitivity, [0.87±0.03] balanced accuracy, [0.87±0.03] F1-score, and [0.917±0.03] AU-ROC score, each at 95% confidence level. The performance of the proposed model is also comparable to the state-of-the-art works. This illustrates the effectiveness of EM-Inception in lung cancer classification. As a future course of work, the eXplainable AI tools could be leveraged to provide deeper insights into the WSIs, such as highlighting the densely tumorous regions