This is the second blog in a series of three covering the iToBoS project’s approach to explainable artificial intelligence (xAI) technical reporting.

Don't miss the iToBoS Workshop on Skin Lesions Detection with 3D-TBP, which will take place at the University of Girona on February 17, 2025.

This is the first of three blogs covering explainable artificial intelligence (xAI) technical reporting in the iToBoS project.

The machine is formed by four robots, each of them moves the vision system device to the points defined by the Vision PC. Each robot will operate in a quadrant of the body map.

Melanoma is one of the most aggressive forms of skin cancer, responsible for 60% of lethal skin neoplasms. Early detection is crucial for improving patient outcomes, particularly as the incidence of melanoma continues to rise, posing an increasing public health challenge due to the aging population and extended life expectancy.

The start, preparation and validation of the data acquisition will be commanded by the HMI, the Hospital Operator Interface. This interface is crucial as it serves as the primary point of control and interaction for medical professionals during the scanning process.

Skin cancer is one of the most prevalent forms of cancer globally, reaching epidemic proportions.

The emergence of AI-generated art within therapeutic settings necessitates the establishment of guidelines to address ethical and privacy concerns including informed consent, data protection, and the confidentiality of patients’ medical images and resultant artworks.

The goal of this competition is to develop state-of-the-art machine learning techniques for detecting multiple skin lesions in clinical images.

The design of the iToBoS scanner places a strong emphasis on patient protection, particularly in the context of its robotic components operating in close proximity to the patient.