191,136 research outputs found

    Zastosowanie sieci neuronowych w diagnostyce patologii endometrium

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    Abstract Aim: The aim of the study was to construct neuron networks utilizing selected risk factors and ultrasonographic (USG) examination parameters in a two-dimensional (2D) and three-dimensional (3D) presentation in relation to endometrial pathologies. Materials and methods: The following risk factors were statistically analyzed: age and menopausal status, parity, using hormonal replacement therapy (HRT), BMI, 2D USG of the endometrium (thickness, uterine artery blood flow indices) and 3D USG (volume, vascularization indices) in relation to the result of histopathological examination of the endometrial tissue in 421 women, aged 22-87 years, with abnormal bleeding from the uterus. The changes of the sensitivity and specificity in the applied models corresponding to changes of the limit value, were presented in the form of receiver operating characteristic curves (ROC) and the comparison of the values of the area under the curve (AUC). The threshold value for the obtained models was established and models of artificial neuron networks (ANN) were constructed on the basis of the ROC. Conclusion: Application of artificial neural networks in medicine has been developing rapidly. They have been applied in pre-surgical differentiation of ovarian tumors and other neoplasms. In case of endometrial carcinoma the degree of clinical usage of artificial neural networks has been limited, despite the fact that, from the mathematical point of view, the differentiation using neural networks would be much more precise than the one that could be obtained by chance.Streszczenie Cel pracy: Celem pracy było skonstruowanie sztucznych sieci neuronowych wykorzystujących wybrane czynniki ryzyka oraz parametry oceny ultrasonograficznej (USG) w prezentacji dwuwymiarowej (2D) i trójwymiarowej (3D) w odniesieniu do patologii endometrium. Materiał i metody: Analizie statystycznej poddano czynniki ryzyka: wiek oraz status menopauzalny, rodność, stosowanie hormonalnej terapii zastępczej, BMI, 2D USG endometrium (grubość, indeksy przepływu krwi w t. macicznej) i 3D (objętość, wskaźniki naczyniowe) w odniesieniu do wyniku badania histopatologicznego z endometrium u 421 kobiet z nieprawidłowym krwawieniem z macicy w wieku 22-87 lat. Zmiany czułości i specyficzności przy przesuwaniu wartości granicznej, dla zastosowanych modeli przedstawiono w formie krzywych ROC (Receiver Operating Characteristic Curves) oraz porównania wartości pola pod badanymi krzywymi AUC (Area Under the Curve). Na podstawie krzywych ROC stwierdzono wartość progową dla uzyskanych modeli oraz skonstruowano modele sztucznych sieci neuronowych (ANN). Wnioski: Wykorzystanie sztucznych sieci neuronowych w medycynie rozwija się dynamicznie. Znalazły one zastosowanie w przedoperacyjnym różnicowaniu guzów jajnika oraz innych nowotworów. W odniesieniu do raka endometrium pomimo tego, że z punktu widzenia matematycznego różnicowanie jest znacznie lepsze niż można by otrzymać przez przypadek, to jednak z punktu widzenia klinicznego w chwili obecnej zastosowanie ich jest ograniczone

    Neuroplasticity of language networks in aphasia: advances, updates, and future challenges

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    Researchers have sought to understand how language is processed in the brain, how brain damage affects language abilities, and what can be expected during the recovery period since the early 19th century. In this review, we first discuss mechanisms of damage and plasticity in the post-stroke brain, both in the acute and the chronic phase of recovery. We then review factors that are associated with recovery. First, we review organism intrinsic variables such as age, lesion volume and location and structural integrity that influence language recovery. Next, we review organism extrinsic factors such as treatment that influence language recovery. Here, we discuss recent advances in our understanding of language recovery and highlight recent work that emphasizes a network perspective of language recovery. Finally, we propose our interpretation of the principles of neuroplasticity, originally proposed by Kleim and Jones (1) in the context of extant literature in aphasia recovery and rehabilitation. Ultimately, we encourage researchers to propose sophisticated intervention studies that bring us closer to the goal of providing precision treatment for patients with aphasia and a better understanding of the neural mechanisms that underlie successful neuroplasticity.P50 DC012283 - NIDCD NIH HHSPublished versio

    Machine Learning and Integrative Analysis of Biomedical Big Data.

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    Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

    Cancer diagnosis using deep learning: A bibliographic review

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    In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

    Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

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    A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.Comment: Evolutionary Computation Journa

    Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

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    Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou
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