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

Apr 10, 2018MIDL 2018 Abstract SubmissionReaders: Everyone
  • 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
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