Publications by authors named "J L Wynne"

Nodular gill disease (NGD) is a serious proliferative gill condition that affects farmed salmonids, particularly in Europe. While the cause of NGD remains unknown (and maybe multifactorial), various amoebae are often isolated from the gills of affected fish and can in some cases be seen associated with lesions by histopathology. The present study aimed to quantify the abundance of different amoeba species directly from the gills of rainbow trout affected by NGD and healthy controls.

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Background: Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformers have achieved competitive performance compared to convolutional neural network (CNN) in deep learning, but they need abundant annotated data for training. Self-supervised learning can effectively leverage unlabeled data to extract useful semantic representations without annotation and its associated costs.

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Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.

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The ability to distinguish between viable and non-viable protozoan parasites is central to improved human and animal health management. While conceptually simple, methods to differentiate cell viability in situ remain challenging. Amoebic gill disease, caused by Neoparamoeba perurans is a parasitic disease impacting Atlantic salmon aquaculture globally.

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Article Synopsis
  • * This study explores the use of radiomics, a novel field focused on extracting information from medical images, to create a predictive model for assessing radiotherapy response within the first three months post-treatment.
  • * Using data from 95 patients and advanced classifiers like random forests and support vector machines, the research identified top radiomic features, achieving an area under the curve (AUC) of 0.829, indicating strong predictive ability for treatment outcomes.
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