An Introduction to Generative Adversarial Networks (GAN)


Generative Adversarial Networks are unsupervised neural networks that are able to analyse information from a dataset and produce similar new samples.

iToBoS project at Liquid Biopsies Congress 2022 (European Association for Cancer Research)


iToBoS project was presented in Liquid Biopsies Congress 2022 of European Association for Cancer Research (EACR).

Economic burden of skin cancers in Europe


As observed by Dr Eline Noels of the Erasmus University of Rotterdam, the knowledge of the economic burden of skin cancers is “essential to enable health policy decision-makers to make well-informed decisions on potential interventions and to be able to evaluate the future effect of these decisions”.

Exosome micro RNAs as liquid biopsy biomarkers to follow-up skin melanomas patients


iToBoS presented through the poster "Exosome micro RNAs as liquid biopsy biomarkers to follow-up skin melanomas patients".

EPLL: image denoising using a Gaussian Mixture Model learned on a large set of patches


For image denoising, there is an important line of work that uses the self-similarity principle that natural images obey: an image contains many image patches similar between each other. To remove noise, one can look for similar patches in an image and average them.

Skin cancers in Europe


In EU 27, melanoma is the 6th type of cancer in terms of incidence (new cases per year) after breast, colorectum, prostate, lung and bladder and the 16th in terms of mortality (yearly deaths).

Data augmentation for automated melanoma lesion detection


Training data balance is crucial to the performance of machine learning (ML) models, especially deep learning models. There would be a high risk of overfitting when training on unbalanced datasets.

iToBoS project in Bootcamp22 Research Strategy


iToBoS project was presented in Bootcamp22 Research Strategy workshop in the framework of the Leibniz University Hannover (LUH) work within the project.

Image Inpainting


Image inpainting is the technique of reconstructing of missing portions in an image in an undetectable way, restoring both texture and structure.

iToBoS project in the World Cancer Research Symposium 2022


iToBoS project was presented at the conference "Environmental/ host factors and clinical heterogeneity in skin melanoma: the need of a holistic diagnostic tool", in the World Cancer Research Symposium (SWCR2022).