Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

2 May 2018Luis C. Garcia-Peraza-HerreraMartin EversonWenqi LiInmanol LuengoLorenz BergerOmer AhmadLaurence LovatHsiu-Po WangWen-Lun WangRehan HaidryDanail StoyanovTom VercauterenSebastien Ourselin

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... (read more)

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