29 citations · 36 across the 11 of their papers we have counts for
12 papers
A vision-language model and platform for temporally mapping surgery from video
Dani Kiyasseh
Mapping surgery is fundamental to developing operative guidelines and enabling autonomous robotic surgery. Recent advances in artificial intelligence (AI) have shown promise in map…
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…
Quantification of Robotic Surgeries with Vision-Based Deep Learning
Dani Kiyasseh, Runzhuo Ma, Taseen F. Haque +4
Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such s…
Turath-150K: Image Database of Arab Heritage
Dani Kiyasseh, Rasheed El-Bouri
Large-scale image databases remain largely biased towards objects and activities encountered in a select few cultures. This absence of culturally-diverse images, which we refer to…
Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David Clifton
Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the…
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…
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…
Severity Detection Tool for Patients with Infectious Disease
Girmaw Abebe Tadesse, Tingting Zhu, Nhan Le Nguyen Thanh +14
Hand, foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low and middle income countries. Tetanus in particular has a high mortality rate and its treatmen…