Technical robustness refers to the capacity of AI systems to be adverse to risks and to behave reliably, minimising and preventing unintentional and unexpected harm. This talk will cover three of the key points that robust AI systems should address: 1) safety and fall-back plans; 2) accuracy; and 3) reliability and reproducibility. We will discuss to which extent these are being addressed in current medical imaging applications, and conclude with an overview of the effect of bias on a system’s robustness, and related mitigation strategies.
Technical robustness and bias in medical imaging
MICCAI 2022, 25th International Conference on Medical Image Computing and Computer Assisted Intervention, 22 September 2022, Singapore, Singapore
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in MICCAI 2022, 25th International Conference on Medical Image Computing and Computer Assisted Intervention, 22 September 2022, Singapore, Singapore and is available at :
PERMALINK : https://www.eurecom.fr/publication/7042