Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach

Jakub M. Tomczak, Maximilian Ilse, Max Welling, Marnix Jansen, H.G Coleman, Marit Lucas, Kikki de Laat, Martijn de Bruin, Henk Marquering, Myrtle J. van der Wel, Onno J. de Boer, C. Dilara Savci Heijink, Sybren L. Meijer

Abstract: In this paper, we hypothesize that morphological properties of nuclei are crucial for classifying dysplastic changes. Therefore, we propose to represent a whole histopathology slide as a collection of smaller images containing patches of nuclei and adjacent tissue. For this purpose, we use a deep multiple instance learning approach. Within this framework we first embed patches in a low-dimensional space using convolutional and fully-connected layers. Next, we combine the low-dimensional embeddings using a multiple instance learning pooling operator and eventually we use fully-connected layers to provide a classification. We evaluate our approach on esophagus cancer histopathology dataset.

Keywords: multiple instance learning, deep learning, esophagus cancer, histopathology

Author Affiliation: University of Amsterdam, University College Hospital London, Queen’s University Belfast, the Academic Medical Center in Amsterdam