Search Results for author:
Found 7 papers, 2 papers with code
A method for quantifying sectoral optic disc pallor in fundus photographs and its association with peripapillary RNFL thickness
Conclusions: We developed software that automatically locates and quantifies disc pallor in fundus photographs and found associations between pallor measurements and pRNFL thickness.
Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT
DL methods can be designed for AIS lesion detection on CT using the vast quantities of routinely-collected CT brain scan data.
Challenges of building medical image datasets for development of deep learning software in stroke
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.
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
Segmentation masks of pathological areas are useful in many medical applications, such as brain tumour and stroke management.
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
We investigate the best way to generate the saliency maps employed in our architecture and propose a way to obtain them from adversarially generated counterfactual images.
SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots
We propose a novel multi-task model that simultaneously predicts dense depth and lumen segmentation with an ensemble of deep networks.
Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis
METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy ageing through to dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.
