An Inception-like CNN Architecture for GI Disease and Anatomical Land...

In this working note, we describe our approach to gastrointestinal

disease and anatomical landmark classification for the Medico task

at MediaEval 2017. We propose an inception-like CNN architecture

and a fixed-crop data augmentation scheme for training and testing.

The architecture is based on GoogLeNet and designed to keep the

number of trainable parameters and its computational overhead

small. Preliminary experiments show that the architecture is able to

learn the classification problem from scratch using a tiny fraction

of the provided training data only.