scispace - formally typeset
E

Ethan Goan

Researcher at Queensland University of Technology

Publications -  11
Citations -  475

Ethan Goan is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 5, co-authored 9 publications receiving 181 citations. Previous affiliations of Ethan Goan include Commonwealth Scientific and Industrial Research Organisation.

Papers
More filters
Journal ArticleDOI

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Thomas Schaffter, +74 more
TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
Book ChapterDOI

Bayesian neural networks: An introduction and survey

TL;DR: Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.
Proceedings ArticleDOI

Plant Disease Detection Using Hyperspectral Imaging

TL;DR: This paper proposes the use of hyperspectral imaging (VNIR and SWIR) and machine learning techniques for the detection of the Tomato Spotted Wilt Virus in capsicum plants and shows excellent discrimination based on the full spectrum and comparable results based on data-driven probabilistic topic models and the domain vegetation indices.
Book ChapterDOI

Bayesian Neural Networks: An Introduction and Survey

TL;DR: Bayesian Neural Networks (BNNs) as mentioned in this paper have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing.
Posted ContentDOI

Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

TL;DR: The use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinson’s Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradyKinesia is described.