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

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 lowdimensional 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.