144 citations · 611 across the 31 of their papers we have counts for
8 papers · 1 filter
PCPs: Patient Cardiac Prototypes
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Many clinical deep learning algorithms are population-based and difficult to interpret. Such properties limit their clinical utility as population-based findings may not generalize…
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
Dani Kiyasseh, Tingting Zhu, David A. Clifton
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease…
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Rasheed el-Bouri, David Eyre, Peter Watkinson +2
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing wit…
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…
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Xinshao Wang, Yang Hua, Elyor Kodirov +2
To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction…
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…