A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling

Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.British Heart Foundation Accelerator Award, UKRoyal Society International Exchanges Cost Share Award, UK RP202G0230Hope Foundation for Cancer Research, UK RM60G0680Medical Research Council Confidence in Concept Award, UK MC_PC_17171MINECO/FEDER, Spain/Europe RTI2018-098913-B100 A-TIC-080-UGR1

Brain tumor MRI medical images classification with data augmentation by transfer learning of VGG16

The ability to estimate conclusions without direct human input in healthcare systems via computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep learning (DL) approaches are already being employed or exploited for healthcare purposes, and in the case of medical images analysis, DL paradigms opened a world of opportunities. This paper describes creating a DL model based on transfer learning of VGG16 that can correctly classify MRI images as either (tumorous) or (non-tumorous). In addition, the model employed data augmentation in order to balance the dataset and increase the number of images. The dataset comes from the brain tumour classification project, which contains publicly available tumorous and non-tumorous images. The result showed that the model performed better with the augmented dataset, with its validation accuracy reaching ~100 %

IEEE Access Special Section Editorial: Advanced Signal Processing Methods in Medical Imaging

Medical Imaging is a technique to create visual representations of the interior of the body, with the aim of making accurate diagnoses and optimized treatments. Many medical imaging techniques are widely used to produce images, such as computer tomography (CT), ultrasound (US), positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI)/functional MRI (fMRI)

Deteção e Quantificação de Microhemorragias Cerebrais com Base em Imagens de Ressonância Magnética Ponderadas por Suscetibilidade Magnética

As microhemorragias cerebrais (CMBs) têm uma função importante no desenvolvimento de hemorragias intracerebrais (ICH) e doenças cerebrovasculares. Estas microestruturas surgem devido ao sangramento perivascular dos pequenos vasos, principalmente afetados por vasculopatia hipertensiva e angiopatia amilóide cerebral, que consistem na forma esporádica da doença dos pequenos vasos (SVD) cerebrais. Esta patologia consiste na segunda maior causa de demência, que por sua vez constituí uma das preocupações a nível global que afeta sobretudo a população idosa. Para que seja possível o diagnóstico precoce, bem como a monitorização da progressão da SVD existe a necessidade do desenvolvimento de um protocolo na prática clínica que agilize o processo de deteção e quantificação de forma automática, rápida e eficiente destes biomarcadores imagiológicos (CMBs) em pacientes com SVD. O processo de inspeção visual de CMBs é na maioria das vezes impraticável em exames de rotina, dado que é bastante demorado. Uma das modalidades de ressonância magnética (RM) com grande potencial para a deteção de CMBs é a imagem ponderada por suscetibilidade magnética (SWI), cuja influência de diversos fatores na quantificação de CMBs ainda necessitam de ser explorados. Assim sendo, nesta tese propôs-se averiguar o potencial de técnicas avançadas de RM, a fim de detetar as CMBs presentes na SVD, incluindo o estudo sistemático de várias opções de préprocessamento das imagens SWI, através da manipulação das máscaras de fase positiva, negativa e sigmóide. Para além da apreciação visual das máscaras de fase procedeu-se à avaliação de algoritmos de aprendizagem automática para a deteção das CMBs. Deste estudo, conclui-se que as imagens SWI pertencentes ao conjunto de dados previamente adquirido podem surgir devido à multiplicação da máscara positiva com a imagem de magnitude quatro vezes. A máscara de fase que proporciona o aumento da sensibilidade na deteção de CMBs é a máscara positiva através de oito multiplicações

Dynamic and Integrative Properties of the Primary Visual Cortex

The ability to derive meaning from complex, ambiguous sensory input requires the integration of information over both space and time, as well as cognitive mechanisms to dynamically shape that integration. We have studied these processes in the primary visual cortex (V1), where neurons have been proposed to integrate visual inputs along a geometric pattern known as the association field (AF). We first used cortical reorganization as a model to investigate the role that a specific network of V1 connections, the long-range horizontal connections, might play in temporal and spatial integration across the AF. When retinal lesions ablate sensory information from portions of the visual field, V1 undergoes a process of reorganization mediated by compensatory changes in the network of horizontal collaterals. The reorganization accompanies the brain’s amazing ability to perceptually “fill-inâ€, or “seeâ€, the lost visual input. We developed a computational model to simulate cortical reorganization and perceptual fill-in mediated by a plexus of horizontal connections that encode the AF. The model reproduces the major features of the perceptual fill-in reported by human subjects with retinal lesions, and it suggests that V1 neurons, empowered by their horizontal connections, underlie both perceptual fill-in and normal integrative mechanisms that are crucial to our visual perception. These results motivated the second prong of our work, which was to experimentally study the normal integration of information in V1. Since psychophysical and physiological studies suggest that spatial interactions in V1 may be under cognitive control, we investigated the integrative properties of V1 neurons under different cognitive states. We performed extracellular recordings from single V1 neurons in macaques that were trained to perform a delayed-match-to-sample contour detection task. We found that the ability of V1 neurons to summate visual inputs from beyond the classical receptive field (cRF) imbues them with selectivity for complex contour shapes, and that neuronal shape selectivity in V1 changed dynamically according to the shapes monkeys were cued to detect. Over the population, V1 encoded subsets of the AF, predicted by the computational model, that shifted as a function of the monkeys’ expectations. These results support the major conclusions of the theoretical work; even more, they reveal a sophisticated mode of form processing, whereby the selectivity of the whole network in V1 is reshaped by cognitive state

Data, deep learning and depression: can artificial neural networks learn risk factors for depression from genetic variants and radiology reports

Major Depressive Disorder (MDD) is a psychiatric disorder characterised by persistent low mood and loss of enjoyment or interest. MDD affects around 1 in 8 people worldwide and is one of the leading causes of global disability. Studies have found both genetic and environmental risk factors. In this thesis automated and scalable models using artificial neural networks are used to analyse two sources of data where risk factors can be found and quantified. A number of genes have a small effect size on MDD, making MDD a polygenic disease. To investigate polygenic diseases, we can analyse Single Nucleotide Polymorphisms (SNPs), base pairs in DNA that commonly differ between individuals. Genome wide association studies (GWAS) are used to quantify the association between SNPs and MDD. From modelling these associations in combination, a Polygenic Risk Score (PRS) can be devised, which quantifies an individual’s genetic risk of MDD. Through scanning the brain using CT or MRI, we can find evidence of disease, including stroke and small vessel disease. A number of brain diseases have been linked to subsequent development of MDD, and combined with genetics could give a better overall risk prediction of developing MDD than either in isolation. This thesis focuses on these two key biological disciplines in MDD research (genetics and imaging) where deep learning, in the form of artificial neural networks, might provide improvement on key problems in these fields. . Specific problems are chosen due to their tractable nature and the ability to benchmark the new techniques against the current state-of-the-art methods. The first project of this thesis uses artificial neural networks that take as input SNP genotypes and output a polygenic risk score for MDD. A number of hyperparameters are tested, as well as different architectures. The best of these models, as chosen by performance (measured using AUC) on a validation set, is then compared on a held-out test set to existing methods including p-value threshold and clump, SBayesR, and LDPred2. The second project uses graph based neural networks, which introduce an additional layer involving a graph, to add structure to the network computation. This structure allows use of existing biological information, in this case data detailing which SNPs act as expression quantitative trait loci (eQTL) for specific genes. A number of graph networks are designed and tested, with the best of these compared to the methods in the first project. Across both the first and second project, the neural network models achieve an AUC, accuracy and Nagelkerke R2 that are comparable to the best of the current methods tested. Additionally, when using ensemble modelling the best performing models included both a neural network based model as well as a summary statistics Bayesian model (LDPred2 or SBayesR). This indicates the neural network models find information not used by the best existing methods, and that an ensemble of models provides the highest performance as defined using the above mentioned metrics. The final project uses neuroradiology reports, which are written reports that accompany radiology scans such as CT or MRI scans, and are used to describe abnormalities that indicate disease. There is evidence that some of the diseases observable in these scans are risk factors for MDD. Part of the processing of the reports needed for further analysis is negation detection, which is the task of deciding if a mention of disease (such as ischaemic stroke) indicates either presence of the disease or lack of presence. An artificial neural network (NN) is developed for this task, and its predictions are assessed against a gold standard labelled by domain experts. The performance of the NN, measured using F1 score, is then compared against that of a rule-based model developed on the same datasets as the NN, and two state-of-the-art rule-based models developed on different datasets. The NN achieves similar performance to the other models, and outperforms the rule-based models not developed on our datasets. Neural networks have previously shown a greater adaptability to new datasets than rule-based methods, thereby demonstrating a potential advantage over rule-based models in transferability between data sources, such as different health boards or studies. The work on this final project has contributed to enabling the automatic annotation of a much larger dataset with increased accuracy. Using this larger dataset further analysis has linked hypertension with increased risk of stroke, as well as baseline depression with increased risk of cerebral small vessel disease. Additionally, approval for access to electronic health records for the entire Scottish population has been granted, and this has been made possible because of the utility and effectiveness of the machine learning approaches. Overall, the deep learning (artificial neural networks) models developed in this thesis are stronger on the negation detection task than the polygenic risk scoring task, performing well against all the models tested and proving useful for processing large datasets for future work. The models developed for assessing genetic risk of MDD currently have more limited use, but deliver results that are comparable to current methods, particularly when summary statistics aren’t available. Additionally, the performance, using AUC and Nagelkerke R2, of the ensemble models indicates the NN models find information in the data unused by the other methods, indicating potential for providing future mechanistic insights. While there are a number of challenges preventing improvements in the predictive performance of NN models, larger samples of individuals with MDD with contemporaneous imaging and genetic data are likely to lead to improvements for these models when used for predictive analytics. This thesis represents a beginning of the work possible with deep learning for MDD research, and these experiments are just a subset of the potential problems where deep learning may provide benefit. The methods used here have the potential to lead to more accurate prediction, further mechanistic insights, and better automation of dataset processing and creation for a number of other problems and challenges in MDD research

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity