Clustering-Based Cancer Diagnosis Model for Whole Slide Image

Automated classification of Whole Slide Images (WSIs) is of great significance for early diagnosis of cancer. Existing approaches are trained on a specific level which affects the analysis performance due to weak supervision of patches and variants. Additionally, it is difficult to distinguish cancer subtype patches accurately from different magnification levels of WSIs. However, this can be improved by employing artificial intelligence models to address these problems, we propose a novel clustering-based cancer diagnosis (CBCD) method for WSI classification. The CBCD constructs three modules: first, we extracted patches from each magnification level of WSIs with respective cancer sub-types. Second, we employed two features (global and local) to learn discriminative and salient information of each patch. Then we find the meaningful cluster regions based on these features to quantify (select) the best patches of salient cancer subtypes by only relying on the collective characteristics of patches from different magnification levels. The clustering techniques used are k-means, gaussian mixture model, and agglomerative clustering. The quality of each clustering technique was determined using adjusted rand, and calinski harabasz scores. Later we used five state-of-the-art (SOTA) deep learning models to learn and classify cancer subtype regions of WSIs based on two types of features of patches. We also showed the results with no clustering techniques in an end-to-end supervised way by directly extracting patches from WSIs. Our method is evaluated on the public WSI dataset (KBSMC) for cancer sub-types classification and achieves better performance and great interpretability compared with the SOTA methods.