[GR] iToBoS – Ευφυής Σαρωτής Ολόκληρου του Δώματος για την Έγκαιρη Διάγνωση Μελανώματος


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".

Skin Cancers: Data and facts


The most updated source for worldwide cancer data is represented by the GLOBOCAN 2020 report of the International Agency of Cancer Research (IACR).

iToBoS findings contribute to UK policy development


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.

Understanding DICOM


DICOM stands for Digital Imaging and COmmunications in Medicine: it is an international standard related to the exchange, storage and communication of digital medical images and other related digital data.

The value of clinical data


Over the last half century, clinicians and researchers have made enormous advances in detecting and treating melanoma.

Uncertainty Estimation in Deep Neural Networksfor Dermoscopic Image Classification


Machine learning and specifically deep learning, have dramatically improved the state-of-the-art in many areas of research, including computer vision, speech recognition, and natural language processing.

Explain and improve: LRP-inference fine-tuning for image captioning models


The work “Explain and improve: LRP-inference fine-tuning for image captioning models”, with the support of iToBoS project, has been published in ScienceDirect.

Ethical Workshop


iToBoS organized the workshop entitled “Ethical impact of the use of AI technologies for the detection of melanoma as addressed in iToBoS”.

IToBoS project held its 2nd General Assembly meeting


The iToBoS 2nd General Assembly (GA) meeting took place on 18th January 2022 throughout video conference system with the attendance of 49 participants belonging to the 19 project partner organizations.

Explaining Machine Learning Models for Clinical Gait Analysis


The scientific work "Explaining Machine Learning Models for Clinical Gait Analysis", with the support of iToBoS project, has been published.