Detection of Lung Cancer Metastases in Lymph Nodes Using a Multiple Instance Learning Approach

Computational tools in pathology become more and more widespread, however, development of such tools usually needs large amounts of data. Furthermore, producing the required annotations can be tedious for pathologists. Previous approaches were able to omit the need for pixel-wise annotations and instead rely on global slide labels. Furthermore, a smart selection of relevant tiles within whole slide images reduces the amount of data needed for training. Such technique is feasible for end-to-end learning. In this paper, a weakly supervised learning algorithm was trained on 668 whole slide images of lymph nodes from lung cancer patients with a nodal disease stage of either N1 or N2. Systematic experiments were designed to explore less complex deep learning models. We evaluated our study on different numbers of representative tiles for each slide. The best performing model scored 84% and 0.903 on accuracy and AUC respectively on a small amount of training data.

Automated summary

This paper explores a weakly supervised learning algorithm for computational tools in pathology. By training on whole slide images of lymph nodes from lung cancer patients, the algorithm achieves high accuracy and AUC. Optimizing the model through smart selection of relevant tiles reduces the amount of training data required.