29 citations · 36 across the 11 of their papers we have counts for
5 papers · 1 filter
SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations
Dani Kiyasseh, Aaron Cohen, Chengsheng Jiang +1
A clinical artificial intelligence (AI) system is often validated on a held-out set of data which it has not been exposed to before (e.g., data from a different hospital with a dis…
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Dani Kiyasseh, Tingting Zhu, David A. Clifton
The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourage…
SoQal: Selective Oracle Questioning in Active Learning
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Large sets of unlabelled data within the healthcare domain remain underutilized. Active learning offers a way to exploit these datasets by iteratively requesting an oracle (e.g. me…
CLOPS: Continual Learning of Physiological Signals
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i.i.d). This viola…
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Clinical settings are often characterized by abundant unlabelled data and limited labelled data. This is typically driven by the high burden placed on oracles (e.g., physicians) to…