Skin cancer affects more than 3 million people only in the US. Comprehensive microscopic databases include around 30 thousand samples, limiting the richness of patterns that can be presented to machine learning. To this end, generative models such as GANs have been proposed for creating realistic synthetic images but, despite their popularity, they are often difficult to train and control. Recently an autoregressive approach based on a quantized autoencoder showed state of the art performances while being simple to train and provide synthetic data generation opportunities. In the first part of this paper we evaluate the training of VQ-VAE-2 with different latent space configuration. In the second part, we show how to use a learned prior over the latent space with PixelSNAIL to generate and modify skin lesions. We show how this process can be used for powerful data augmentation and visualization for skin health, evaluating it on a downstream application that classifies malignant lesions
In this paper we address the problem of privacy protection and trust enhancement in a distributed healthcare eco system.
Increased trust in other parties of the eco system encourages medical entities to share data. This increases the availability
of data and consequently improves the general quality of health care. We present two different solutions to the above
described problem, both being developed using the DICOM standard (Digital Imaging and Communications in
Medicine). The first approach, which is partially relying on legislation, uses sticky policies and commitment protocols to
enhance trust. We propose to attach the access control policies to the data in the DICOM files. Furthermore, the source
of data disclosure makes sure that the destination commits to enforce the policies by obtaining a signature on the policies
and thus providing a proof of the commitment by the destination. The second approach aims at increasing trust by
technical enforcement. For this purpose, digital rights management (DRM) technology is used. We demonstrate that it is
possible to create a DICOM DRM container using the tools provided by this standard, hence still guaranteeing backward
compatibility.
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