In this blogpost we will talk about the importance of image segmentation for dermoscopy as well as other medical imaging techniques. For skin cancer screening it is of importance to segment the nevus from the skin.

Image Super-Resolution is the task of restoring a high-resolution image out of one or several lower resolution images. A simple enlargement of the image would produce a blurred result, so a special approach is needed to boost the apparent resolution and enhance image sharpness.

Image deblurring is the technique of removing blurring artifacts from an image that can come from object motion, camera shake or out-of-focus blur.

iToBoS project was presented in BioTech Spain, an online portal dedicated to the dissemination and diffusion of projects and news related to biotechnology in Spain.

The scientific work "Explaining the Predictions of Unsupervised Learning Models", developed with the support of the iToBoS project, has been published.

Image acquisition comes with unavoidable and unwanted noise acquisition due to camera hardware limitations and illumination challenges, making image denoising a fundamental task in image processing.

In the last few years, there have been remarkable developments in computational methods for helping dermatologists to diagnose skin cancer in early stages.

Solving increasingly complex real-world problems, continuously contributes to the success of deep neural networks (DNNs) (Schütt et al. 2017; Senior et al. 2020). DNNs have long been established in numerous machine learning tasks and for this have been significantly improved in the past decade.

The scientific work "Explainable AI Methods - A Brief Overview", with the support of iToBoS project, has been published.

The scientific work "ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs", with the support of iToBoS project, has been published.