Data classification is the process of classifying data as a whole (e.g. database schema) or its parts (e.g. column name, column values) into categories. It can also be evaluated for its identifiability, sensitivity and/or confidentiality. In this work, our focus lies in and around structured (and semi structured) data.
The work "Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations", supported by iToBoS project, has been published.
The scientific work "PatClarC: Using pattern concept activation vectors for noise-robust model debugging", with the support of iToBoS project, has been published.
In the iToBoS project, the so-called 3D cameras are used. These cameras enable us to make a virtual representation of the patient. This can help the dermatologists to match dermoscopic images of lesions with the lesions on the body of the patient.
Skin cancer is one of the most common cancers in the world. Late-stage skin cancers spreads to internal organs and become fatal. Early-stage skin cancer can be cured with a high survival rate, while the 5-year survival rate for skin cancer is extremely low. Therefore, early detection is the key in fighting skin cancer.
Το iToBoS είναι ένα ερευνητικό έργο που χρηματοδοτείται από το πρόγραμμα έρευνας και καινοτομίας της Ευρωπαϊκής Επιτροπής Horizon 2020, στο θέμα "Ψηφιακή διαγνωστική - Ανάπτυξη εργαλείων για την υποστήριξη κλινικών αποφάσεων ενσωματώνοντας διάφορα είδη διαγνωστικών δεδομένων".
iToBoS partner, Trilateral Research, recently contributed to a UK government call for policy advice pertaining to the risks, benefits, and ethical and legal considerations of medical data sharing.
The scientific work "Finding and removing Clever Hans: Using explanation methods to debug and improve deep models", with the support of iToBoS project, has been published.