48 Result(s)
within
Chapter
Yalin Zheng
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Chapter and Conference Paper
Weakly/Semi-supervised Left Ventricle Segmentation in 2D Echocardiography with Uncertain Region-Aware Contrastive Learning
Segmentation of the left ventricle in 2D echocardiography is essential for cardiac function measures, such as ejection fraction. Fully-supervised algorithms have been used in the past to segment the left ventr...
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Chapter and Conference Paper
Polar-Net: A Clinical-Friendly Model for Alzheimer’s Disease Detection in OCTA Images
Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as ...
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Chapter and Conference Paper
Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-Focused nnU-Net
Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF. In this paper, w...
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Chapter and Conference Paper
Electrocardiogram Two-Dimensional Motifs: A Study Directed at Cardio Vascular Disease Classification
A process is described, using the concept of 2D motifs and 2D discords, to build classification models to classify Cardiovascular Disease using Electrocardiogram (ECG) data as the primary input. The motivation...
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Chapter and Conference Paper
Correction to: Ophthalmic Medical Image Analysis
In an older version of this chapter, the title was incomplete. This has been corrected.
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Chapter and Conference Paper
Shape-Aware Weakly/Semi-Supervised Optic Disc and Cup Segmentation with Regional/Marginal Consistency
Glaucoma is a chronic eye disease that permanently impairs vision. Vertical cup to disc ratio (vCDR) is essential for glaucoma screening. Thus, accurately segmenting the optic disc (OD) and optic cup (OC) from co...
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Chapter and Conference Paper
NerveFormer: A Cross-Sample Aggregation Network for Corneal Nerve Segmentation
The segmentation of corneal nerves in corneal confocal microscopy (CCM) is of great to the quantification of clinical parameters in the diagnosis of eye-related diseases and systematic diseases. Existing work...
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Chapter
Application of Deep Learning Models in the Evaluation of Histopathology of Uveal Melanoma
Digital pathology is being increasingly implemented in basic and translational research, as well as in routine clinical care for diagnostics and prognostication. With the latter, it is essentially an additiona...
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Chapter and Conference Paper
Scanned ECG Arrhythmia Classification Using a Pre-trained Convolutional Neural Network as a Feature Extractor
The classification of cardiovascular diseases using ECG data is considered. It is argued that to obtain a satisfactory classification features should be extracted from ECG images in their entirety, instead of ...
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Chapter and Conference Paper
End-to-End Deep Learning Vector Autoregressive Prognostic Models to Predict Disease Progression with Uneven Time Intervals
We propose an end-to-end deep learning method combining implicit feature extraction and an autoregressive model to predict the future course of a disease or condition. By merging the feature extraction and aut...
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Chapter and Conference Paper
Addressing the Challenge of Data Heterogeneity Using a Homogeneous Feature Vector Representation: A Study Using Time Series and Cardiovascular Disease Classification
An investigation into the use of a unifying Homogeneous Feature Vector Representation (HFVR), to address the challenge of applying machine learning and/or deep learning to heterogeneous data, is presented. To ...
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Chapter and Conference Paper
Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation
Morphological changes, e.g. thickness of retinal or choroidal layers in Optical coherence tomography (OCT), is of great importance in clinic applications as they reveal some specific eye diseases and other sys...
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Chapter and Conference Paper
Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography
Optical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in cli...
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Chapter and Conference Paper
TransBridge: A Lightweight Transformer for Left Ventricle Segmentation in Echocardiography
Echocardiography is an essential diagnostic method to assess cardiac functions. However, manually labelling the left ventricle region on echocardiography images is time-consuming and subject to observer bias. ...
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Chapter and Conference Paper
Motif Based Feature Vectors: Towards a Homogeneous Data Representation for Cardiovascular Diseases Classification
A process for generating a unifying motif-based homogeneous feature vector representation is described and evaluated. The motivation was to determine the viability of this representation as a unifying represen...
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Chapter and Conference Paper
Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration
Affine registration has recently been formulated using deep learning frameworks to establish spatial correspondences between different images. In this work, we propose a new unsupervised model that investigate...
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Chapter and Conference Paper
Joint Destri** and Segmentation of OCTA Images
As an innovative retinal imaging technology, optical coherence tomography angiography (OCTA) can resolve and provide important information of fine retinal vessels in a non-invasive and non-contact way. The ef...
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Chapter and Conference Paper
DAISY Descriptors Combined with Deep Learning to Diagnose Retinal Disease from High Resolution 2D OCT Images
Optical Coherence Tomography (OCT) is commonly used to visualise tissue composition of the retina. Previously, deep learning has been used to analyse OCT images to automatically classify scans by the disease ...
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Chapter and Conference Paper
Deep Vectorization Convolutional Neural Networks for Denoising in Mammogram Using Enhanced Image
Mammography is an X-ray image of the breast which has been widely used for the management of breast cancer. However, in many cases, it is not easy to identify a sign of cancer as tumour or malignancy due to cl...
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Chapter and Conference Paper
Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images
Automated classification of retinal artery (A) and vein (V) is of great importance for the management of eye diseases and systemic diseases. Traditional colour fundus images usually provide a large field of vi...
