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Found 12 papers, 3 papers with code
SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations
The ability to triage unreliable predictions for further inspection and assess the algorithmic bias of AI systems can improve the integrity of research findings and contribute to the deployment of ethical AI systems in medicine.
Quantification of Robotic Surgeries with Vision-Based Deep Learning
We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes.
Turath-150K: Image Database of Arab Heritage
As a consequence of Turath, we hope to engage machine learning researchers in under-represented regions, and to inspire the release of additional culture-focused databases.
Cultural Vocal Bursts Intensity Prediction
Image Classification
Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
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 form of a clinical report, a cumbersome process that is prone to errors.
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals
The ubiquity and rate of collection of physiological signals produce large, unlabelled datasets.
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks.
PCPs: Patient Cardiac Prototypes
Many clinical deep learning algorithms are population-based and difficult to interpret.
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes
The ongoing digitization of health records within the healthcare industry results in large-scale datasets.
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another.
SoQal: Selective Oracle Questioning in Active Learning
Large sets of unlabelled data within the healthcare domain remain underutilized.
CLOPS: Continual Learning of Physiological Signals
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i. i. d).
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.
