In this work, we have concentrated our efforts on the interpretability of
classification results coming from a fully convolutional neural network.
Motivated by the classification of oesophageal tissue for real-time detection
of early squamous neoplasia, the most frequent kind of oesophageal cancer in
Asia, we present a new dataset and a novel deep learning method that by means
of deep supervision and a newly introduced concept, the embedded Class
Activation Map (eCAM), focuses on the interpretability of results as a design
constraint of a convolutional network. We present a new approach to visualise
attention that aims to give some insights on those areas of the oesophageal
tissue that lead a network to conclude that the images belong to a particular
class and compare them with those visual features employed by clinicians to
produce a clinical diagnosis. In comparison to a baseline method which does not
feature deep supervision but provides attention by grafting Class Activation
Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed
attention maps
