1,045 research outputs found
Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases
Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers
Pattern classification approaches for breast cancer identification via MRI: stateāofātheāart and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current stateāofātheāart computerāaided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multiāparametric
computerāaided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semiāsupervised deep learning and selfāsupervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a highādimensional medical imaging analysis platform that is based on multiātask
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCEāMRI. Since some of the approaches discussed are also based on
timeālapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis
Computational models and approaches for lung cancer diagnosis
The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results
Computational diagnosis and risk evaluation for canine lymphoma
The canine lymphoma blood test detects the levels of two biomarkers, the
acute phase proteins (C-Reactive Protein and Haptoglobin). This test can be
used for diagnostics, for screening, and for remission monitoring as well. We
analyze clinical data, test various machine learning methods and select the
best approach to these problems. Three family of methods, decision trees, kNN
(including advanced and adaptive kNN) and probability density evaluation with
radial basis functions, are used for classification and risk estimation.
Several pre-processing approaches were implemented and compared. The best of
them are used to create the diagnostic system. For the differential diagnosis
the best solution gives the sensitivity and specificity of 83.5% and 77%,
respectively (using three input features, CRP, Haptoglobin and standard
clinical symptom). For the screening task, the decision tree method provides
the best result, with sensitivity and specificity of 81.4% and >99%,
respectively (using the same input features). If the clinical symptoms
(Lymphadenopathy) are considered as unknown then a decision tree with CRP and
Hapt only provides sensitivity 69% and specificity 83.5%. The lymphoma risk
evaluation problem is formulated and solved. The best models are selected as
the system for computational lymphoma diagnosis and evaluation the risk of
lymphoma as well. These methods are implemented into a special web-accessed
software and are applied to problem of monitoring dogs with lymphoma after
treatment. It detects recurrence of lymphoma up to two months prior to the
appearance of clinical signs. The risk map visualisation provides a friendly
tool for explanatory data analysis.Comment: 24 pages, 86 references in the bibliography, Significantly extended
version with review of lymphoma biomarkers and data mining methods (Three new
sections are added: 1.1. Biomarkers for canine lymphoma, 1.2. Acute phase
proteins as lymphoma biomarkers and 3.1. Data mining methods for biomarker
cancer diagnosis. Flowcharts of data analysis are included as supplementary
material (20 pages
Performance of GAN-based augmentation for deep learning COVID-19 image classification
The biggest challenge in the application of deep learning to the medical
domain is the availability of training data. Data augmentation is a typical
methodology used in machine learning when confronted with a limited data set.
In a classical approach image transformations i.e. rotations, cropping and
brightness changes are used. In this work, a StyleGAN2-ADA model of Generative
Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
After assessing the quality of generated images they are used to increase the
training data set improving its balance between classes. We consider the
multi-class classification problem of chest X-ray images including the COVID-19
positive class that hasn't been yet thoroughly explored in the literature.
Results of transfer learning-based classification of COVID-19 chest X-ray
images are presented. The performance of several deep convolutional neural
network models is compared. The impact on the detection performance of
classical image augmentations i.e. rotations, cropping, and brightness changes
are studied. Furthermore, classical image augmentation is compared with
GAN-based augmentation. The most accurate model is an EfficientNet-B0 with an
accuracy of 90.2 percent, trained on a dataset with a simple class balancing.
The GAN augmentation approach is found to be subpar to classical methods for
the considered dataset.Comment: To be published in prceedings of WMLQ2022 International Workshop on
Machine Learning and Quantum Computing Applications in Medicine and Physic
Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.
The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction
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