30 research outputs found
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to
generate a high-resolution (HR) HSI with higher spectral and spatial fidelity
from its low-resolution (LR) counterpart. The generative adversarial network
(GAN) has proven to be an effective deep learning framework for image
super-resolution. However, the optimisation process of existing GAN-based
models frequently suffers from the problem of mode collapse, leading to the
limited capacity of spectral-spatial invariant reconstruction. This may cause
the spectral-spatial distortion on the generated HSI, especially with a large
upscaling factor. To alleviate the problem of mode collapse, this work has
proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can
map the generated spectral-spatial features from the image space to the latent
space and produce a coupling component to regularise the generated samples.
Essentially, we treat an HSI as a high-dimensional manifold embedded in a
latent space. Thus, the optimisation of GAN models is converted to the problem
of learning the distributions of high-resolution HSI samples in the latent
space, making the distributions of the generated super-resolution HSIs closer
to those of their original high-resolution counterparts. We have conducted
experimental evaluations on the model performance of super-resolution and its
capability in alleviating mode collapse. The proposed approach has been tested
and validated based on two real HSI datasets with different sensors (i.e.
AVIRIS and UHD-185) for various upscaling factors and added noise levels, and
compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR,
BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure
Larger Chinese text spacing and size: effects on older users' experience
With declining vision ability, character spacing and size on smartphones designed for the general population are not accessible for older adults. This study aimed to explore how larger Chinese character spacing and size affect older adults’ user experience (UX). An orthogonal experiment was conducted. The optimal range of font size (FS), word spacing (WS) and line spacing (LS) were proposed utilising subjective evaluations to investigate the correlation of eye movement data with participants perceived UX. The results showed that improvement in different aspects of UX varied when FS, WS and LS increased. Overall, participants preferred larger FS, WS and LS, however, the larger FS, WS and LS values are more likely to cause errors and slower reading speed. These results suggest that the distinct combination of size and spacing depends on the motivation, needs and situation of older people when reading on a smartphone. These findings will help designers to provide better design for the older people
An automated cloud-based big data analytics platform for customer insights
Product reviews have a significant influence
on strategic decisions for both businesses and customers on
what to produce or buy. However, with the availability of
large amounts of online information, manual analysis of
reviews is costly and time consuming, as well as being
subjective and prone to error. In this work, we present an
automated scalable cloud-based system to harness big
customer reviews on products for customer insights
through data pipeline from data acquisition, analysis to
visualisation in an efficient way. The experimental
evaluation has shown that the proposed system achieves
good performance in terms of accuracy and computing
time
A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs
This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson’s ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated
A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery
Spectral-spatial-based deep learning models have recently proven to be effective in hyper-spectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of ``black-box'' model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model--a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e., biophysical and biochemical attributes and their hierarchical structures of target entities)-based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI-based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI data sets for four separate tasks (i.e., plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models. The results demonstrate that the proposed model has competitive advantages in terms of both classification accuracy and model interpretability, especially for vegetation classification
Detection and modelling of contacts in explicit finite-element simulation of soft tissue biomechanics
Realistic modelling of soft-tissue biomechanics and mechanical interactions between tissues is an important part of surgical simulation, and may become a valuable asset in
surgical image-guidance. Unfortunately, it is also computationally very demanding. Explicit
matrix-free FEM solvers have been shown to be a good choice for fast tissue simulation,
however little work has been done on contact algorithms for such FEM solvers.
This work introduces such an algorithm that is capable of handling the scenarios typically encountered in image-guidance. The responses are computed with an evolution of
the Lagrange-multiplier method first used by Taylor and Flanagan in PRONTO 3D with
spatio-temporal smoothing heuristics for improved stability with coarser meshes and larger
time steps. For contact search, a bounding-volume hierarchy (BVH) capable of identifying self collisions, and which is optimised for the small time steps by reducing the number
of bounding-volume refittings between iterations through identification of geometry areas
with mostly rigid motion and negligible deformation, is introduced. Further optimisation is
achieved by integrating the self-collision criterion in the BVH creation and updating algorithms.
The effectiveness of the algorithm is demonstrated on a number of artificial test cases
and meshes derived from medical image data
CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia from Chest X-ray Images
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. The Average Accuracy, Sensitivity, Specificity, PPV and F1-score of models in the COVID-19 pneumonia detection reach 0.992, 0.998, 0.985 and 0.989, respectively. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis
CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia from Chest X-ray Images
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. The Average Accuracy, Sensitivity, Specificity, PPV and F1-score of models in the COVID-19 pneumonia detection reach 0.992, 0.998, 0.985 and 0.989, respectively. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis
A deep learning-based approach for automated yellow rust disease detection from high resolution hyperspectral UAV images
Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in Unmanned Aerial Vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images
NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics
Purpose
NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.
Methods
The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C ++++ , and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit’s usage in biomedical applications are provided.
Results
Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.
Conclusion
The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications