10 citations · 18 across the 6 of their papers we have counts for
6 papers
Pre-processing and quality control of large clinical CT head datasets for intracranial arterial calcification segmentation
Benjamin Jin, Maria del C. Valdés Hernández, Alessandro Fontanella +6
As a potential non-invasive biomarker for ischaemic stroke, intracranial arterial calcification (IAC) could be used for stroke risk assessment on CT head scans routinely acquired f…
Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT
Alessandro Fontanella, Wenwen Li, Grant Mair +6
Computed Tomography (CT) is commonly used to image acute ischemic stroke (AIS) patients, but its interpretation by radiologists is time-consuming and subject to inter-observer vari…
Challenges of building medical image datasets for development of deep learning software in stroke
Alessandro Fontanella, Wenwen Li, Grant Mair +7
Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited. Furthermore, the dat…
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
Alessandro Fontanella, Antreas Antoniou, Wenwen Li +4
In some medical imaging tasks and other settings where only small parts of the image are informative for the classification task, traditional CNNs can sometimes struggle to general…
Domain Independent SVM for Transfer Learning in Brain Decoding
Shuo Zhou, Wenwen Li, Christopher R. Cox +1
Brain imaging data are important in brain sciences yet expensive to obtain, with big volume (i.e., large p) but small sample size (i.e., small n). To tackle this problem, transfer…
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Wenwen Li, Jian Lou, Shuo Zhou +1
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional st…