Julien Lerouge


IODA: An input/output deep architecture for image labeling

1Normandie Université, LITIS (EA 4108-FR CNRS 3638), INSA de Rouen, 76800 Saint-Étienne-du-Rouvray, France
2Department of Clinical Hematology and INSERM U918 Unit, Henri Becquerel Cancer Center, 76000 Rouen, France
3Department of Nuclear Medicine, Henri Becquerel Cancer Center and Rouen University Hospital, 76000 Rouen, France

Abstract :

In this paper, we propose a deep neural network (DNN) architecture called Input Output Deep Architecture (IODA) for solving the problem of image labeling. IODA directly links a whole image to a whole label map, assigning a label to each pixel using a single neural network forward step.

Instead of designing a handcrafted a priori model on labels (such as an atlas in the medical domain), we propose to automatically learn the dependencies between labels. The originality of IODA is to transpose DNN input pre-training trick to the output space, in order to learn a high level representation of labels. It allows a fast image labeling inside a fully neural network framework, without the need of any preprocessing such as feature designing or output coding.

In this paper, IODA is applied on both a toy texture problem and a real-world medical image dataset, showing promising results. We provide an open source implementation of IODA.