3,502 research outputs found
The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences.
Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data
The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing
In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing
Grid Analysis of Radiological Data
IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services
Classification of dementias based on brain radiomics features
Dissertação de mestrado integrado em Engenharia InformáticaNeurodegenerative diseases impair the functioning of the brain and are characterized by alterations in
the morphology of specific brain regions. Some of the main disorders include Alzheimer's, Parkinson's,
and Huntington's diseases, and the number of cases increases exponentially since ageing is one of the
main risk factors. Trying to identify the areas in which this type of disease appears is something that
can have a very positive impact in this area of Medicine and can guarantee a more appropriate
treatment or allow the improvement of the quality of life of patients. With the current technological
advances, computer tools are capable of performing a structural or functional analysis of neuroimaging
data from Magnetic Resonance Images(MRI). Therefore, Medical Informatics uses these techniques to
create and manage medical neuroimaging data to improve the diagnosis and management of these
patients. MRI is the image type used in the analysis of the brain area and points to a promising and
reliable diagnostic tool since it allows high-quality images in various planes or strategies and MRI
methods are fundamental diagnostic tools in clinical practice, allowing the diagnosis of pathologic
processes such as stroke or brain tumours. However, structural MRI has limitations for the diagnosis of
neurodegenerative disorders since it mainly identifies atrophy of brain regions.
Currently, there is increased interest in informatics applications capable of monitoring and quantifying
human brain imaging alterations, with potential for neurodegenerative disorders diagnosis and
monitoring. One of these applications is Radiomics, which corresponds to a methodolog ythat allows the
extraction of features from images of a given region of the brain. Specific quantitative metrics from MRI
are acquired by this tool, and they correspond to a set of features, including texture, shape, among
others. To standardize Radiomics application, specific libraries have been proposed to be used by the
bioinformatics and biomedical communities, such as PyRadiomics, which corresponds to an open source Python package for extracting Radiomics of MRIs.
Therefore, this dissertation was developed based on magnetic resonance images and the study of Deep
Learning (DL) techniques to assist researchers and neuroradiologists in the diagnosis and prediction of
neurodegenerative disease development. Two different main tasks were made: first, a segmentation,
using FreeSurfer, of different regions of the brain and then, a model was build from radiomic features
extracted from each part of the brain and interpreted for knowledge extraction.As doenças neurodegenerativas estão associadas ao funcionamento do cérebro e caracterizam-se pelo
facto de serem altamente incapacitantes. São exemplos destas, as doenças de Alzheimer, Parkinson e
Huntington, e o seu número de casos tem vindo a aumentar exponencialmente, uma vez que o
envelhecimento é um dos principais factores de risco. Tentar identificar quais são as regiões cerebrais
que permitem predizer o seu aparecimento e desenvolvimento é algo que, sendo possível, terá um
impacto muito positivo nesta área da Medicina e poderá garantir um tratamento mais adequado, ou
simplesmente melhorar a qualidade de vida dos pacientes. Com os avanços tecnológicos atuais, foram
desenvolvidas ferramentas informáticas que são capazes de efetuar uma análise estrutural ou funcional
de Ressonâncias Magnéticas (MRI), sendo essas ferramentas usadas para promover a melhoria e o
conhecimento clínico. Deste modo, as constantes evoluções científicas têm realçado o papel da
Informática Médica na neuroimagem para criar e gerenciar dados médicos, melhorando o diagnóstico
destes pacientes.
A MRI é o tipo de imagem utilizada na análise de regiões cerebrais e aponta para uma ferramenta de
diagnóstico promissora e fiável, uma vez que permite obter imagens de alta qualidade em vários
planos, permitindo assim, o diagnóstico de processos patológicos, tais como acidentes vasculares ou
tumores cerebrais.
Atualmente, existem inúmeras aplicações informáticas capazes de efetuar análises estruturais e
funcionais do cérebro humano, pois é este o principal órgão afetado pelas doenças
neurodegenerativas. Uma dessas aplicações é o Radiomics, que permite fazer a extração de features
de imagens do cérebro. A biblioteca a utilizar será PyRadiomics, que corresponde a um package open source em Python para a extração de features Radiomics de imagens médicas. As features
correspondem a características da imagem.
Assim sendo, a presente dissertação foi desenvolvida com base em imagens de ressonância magnética
e no estudo das técnicas de Deep Learning para investigar e auxiliar os médicos neurorradiologistas a
diagnosticar e a prever o desenvolvimento de doenças neurodegenerativas. Foram feitas duas
principais tarefas: primeiro, uma segmentação, utilizando o software FreeSurfer, de diferentes regiões
do cérebro e, de seguida, foi construído um modelo a partir das features radiómicas extraídas de cada
parte do cérebro que foi interpretado
3D Medical Image Lossless Compressor Using Deep Learning Approaches
The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling
Ophthalmic Diseases Classification Based on YOLOv8
With the rising prevalence of retinal diseases, identifying eye diseases at an early stage is crucial for effective treatment and prevention of irreversible blindness. But Ophthalmologists face challenges in detecting subtle symptoms that may indicate the presence of a disease before it progresses to an advanced stage Among these challenges, eye diseases can present with a wide range of symptoms, and some conditions may share similar signs. To solve these difficulties, in the research proposed YOLOV8(You Only Look Once) Lightweight Self-Attention model to classify seven different retinal diseases. In this regard, the dataset that have been used in this study contains 5787 images from three different sources (Roboflow, Kaggle and Medical Clinics) were included in the seven classes of Glaucoma, Age-related Macular Degeneration (AMD), Cataract, Diabetic retinopathy (DR), and Retinal Vein Occlusion, which comprises of Branch Retinal Vein Occlusion (BRVO) and Central Retinal Occlusion (CRVO) and normal. As a results, the model has proven excellent performance in its classification ability. Boasting an average classification accuracy of 94% across the seven disease with precsition 96.2%, recall 96.6%and f1 score was 96.3% At the time of training it was 0.6 Houres(H). When compaired with Resnet50, VGG16 results underscore the model’s superior performance in precision and computational efficiency compared. The algorithm's evaluation reveals its superiority when compared to earlier pertinent research, making it a trustworthy method for classifying retinal illnesses
JOINT CODING OF MULTIMODAL BIOMEDICAL IMAGES US ING CONVOLUTIONAL NEURAL NETWORKS
The massive volume of data generated daily by the gathering of medical images with
different modalities might be difficult to store in medical facilities and share through
communication networks. To alleviate this issue, efficient compression methods
must be implemented to reduce the amount of storage and transmission resources
required in such applications. However, since the preservation of all image details
is highly important in the medical context, the use of lossless image compression
algorithms is of utmost importance.
This thesis presents the research results on a lossless compression scheme designed
to encode both computerized tomography (CT) and positron emission tomography
(PET). Different techniques, such as image-to-image translation, intra prediction,
and inter prediction are used. Redundancies between both image modalities are
also investigated. To perform the image-to-image translation approach, we resort to
lossless compression of the original CT data and apply a cross-modality image translation
generative adversarial network to obtain an estimation of the corresponding
PET.
Two approaches were implemented and evaluated to determine a PET residue
that will be compressed along with the original CT. In the first method, the
residue resulting from the differences between the original PET and its estimation
is encoded, whereas in the second method, the residue is obtained using encoders
inter-prediction coding tools. Thus, in alternative to compressing two independent
picture modalities, i.e., both images of the original PET-CT pair solely the CT is
independently encoded alongside with the PET residue, in the proposed method.
Along with the proposed pipeline, a post-processing optimization algorithm that
modifies the estimated PET image by altering the contrast and rescaling the image
is implemented to maximize the compression efficiency.
Four different versions (subsets) of a publicly available PET-CT pair dataset
were tested. The first proposed subset was used to demonstrate that the concept
developed in this work is capable of surpassing the traditional compression schemes.
The obtained results showed gains of up to 8.9% using the HEVC. On the other
side, JPEG2k proved not to be the most suitable as it failed to obtain good results,
having reached only -9.1% compression gain. For the remaining (more challenging) subsets, the results reveal that the proposed refined post-processing scheme attains,
when compared to conventional compression methods, up 6.33% compression gain
using HEVC, and 7.78% using VVC
Privacy-Protecting Techniques for Behavioral Data: A Survey
Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved
- …