Authors:
Clara Brémond-Martin
;
Huaqian Wu
;
Cédric Clouchoux
and
Kévin François-Bouaou
Affiliation:
Witsee, 33 Av. des Champs-Élysées, 75008 Paris, France
Keyword(s):
GAN, Single Input, Auto-Encoder, Biomedical, Pair, Segmentation.
Abstract:
Generating synthetic pairs of raw and ground truth (GT) image is a strategy to reduce the amount of acquisition and annotation by biomedical experts. Pair image generation strategies, from single-input paired images (SIP), focus on patch-pyramid (PP) or on dual branch generator but, resulting synthetic images are not natural. With few-input images, for raw synthesis, adversarial auto-encoders synthesises more natural images. Here we propose Pair-GAN, a combination of PP containing auto-encoder generators at each level, for the biomedical image synthesis based upon a SIP. PP allows to synthesise using SIP while the AAE generator renders most natural the image content. We use for this work two biomedical datasets containing raw and GT images. Our architecture is evaluated with seven state of the art method updated for SIP: qualitative, similitude and segmentation metrics, Kullback Leibler divergences from synthetic and original feature image representations, computational costs and sta
tistical analyses. Pair-GAN generates most qualitative and natural outputs, similar to original pairs with complex shape not produced by other methods, however with increased memory needs. Future works may use this generative procedure for multimodal biomedical dataset synthesis to help their automatic processing such as classification or segmentation with deep learning tools.
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