Search Results for author:
Found 14 papers, 1 papers with code
Translating Simulation Images to X-ray Images via Multi-Scale Semantic Matching
In this paper, we propose a new method to translate simulation images from an endovascular simulator to X-ray images.
KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study
Sleep behaviour and in-bed movements contain rich information on the neurophysiological health of people, and have a direct link to the general well-being and quality of life.
CNN-based Classification Framework for Lung Tissues with Auxiliary Information
Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis.
Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies
Additionally, a new metric together with data visualisations are employed to extract meaningful insights from the postures dataset, demonstrate the added value of the data augmentation method, and explain the classification performance.
A Robust Framework of Chromosome Straightening with ViT-Patch GAN
The generator learns the motion representation of chromosomes for straightening.
Bilateral-ViT for Robust Fovea Localization
The fovea is an important anatomical landmark of the retina.
A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image
To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details.
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction
We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction.
Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering
The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph.
Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering
The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph.
Contextualised Graph Attention for Improved Relation Extraction
This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction.
Joint Multi-Label Attention Networks for Social Text Annotation
To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content.
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction.
