As we approach the end of the year, we want to take a moment to reflect on the journey and some achievements of the Intelligent Body Scanner for Early Detection of Melanoma (iToBoS) project throughout 2023.

The iToBoS project is having a significant media impact.

How we experience music is an interesting topic in neuroscience, and with functional magnetic resonance imaging (fMRI) is possible the exciting task of reconstructing music from brain activity.

The goal of this project is to help protect the privacy of customers by removing tattoos that would enable them to be identified. This is an important issue when managing personal images, as is the case with the iToBoS.

Image-to-image translation models can output a high-resolution generated image given a 2D label map as an input.

Here at iToBoS, we strongly suggest you avoid getting a suntan. You certainly need some sunshine in your life, but UV dose high enough to cause a tan is already much higher than the dose needed for vitamin D production.

EfficientNet was first proposed in the original paper of Mingxing Tan & Quoc V Le in 2019, namely EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

iToBoS is exploring the use of EfficientNet models for pigmented skin lesion classification. This family of models, developed by Google researchers, are deep learning architectures that offer exceptional performance.

Test datasets serve as a benchmark to evaluate the performance of algorithms and models.

I-JEPA [1], the latest self-supervised model from Meta AI, has been officially released: the paper was published in June 2023 at the last CVPR conference, together with their codes that they made open-source.