no code implementations • 2 Jan 2024 • Dani Kiyasseh, Aaron Cohen, Chengsheng Jiang, Nicholas Altieri
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.
no code implementations • 6 May 2022 • Dani Kiyasseh, Runzhuo Ma, Taseen F. Haque, Jessica Nguyen, Christian Wagner, Animashree Anandkumar, Andrew J. Hung
We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes.
no code implementations • 1 Jan 2022 • Dani Kiyasseh, Rasheed el-Bouri
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
1 code implementation • 19 Mar 2021 • 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 form of a clinical report, a cumbersome process that is prone to errors.
no code implementations • 1 Jan 2021 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
The ubiquity and rate of collection of physiological signals produce large, unlabelled datasets.
no code implementations • NeurIPS 2021 • 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.
no code implementations • 28 Nov 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
Many clinical deep learning algorithms are population-based and difficult to interpret.
no code implementations • 28 Sep 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
The ongoing digitization of health records within the healthcare industry results in large-scale datasets.
1 code implementation • 27 May 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
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.
no code implementations • 22 Apr 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
Large sets of unlabelled data within the healthcare domain remain underutilized.
no code implementations • 20 Apr 2020 • 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).
2 code implementations • 20 Apr 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.