305 research outputs found
On the Use of Deep Learning Decompositions and Physiological Measurements for the Prediction of Preterm Pregnancies in a Cohort of Patients in Active Labor â€
Preterm pregnancies are one of the leading causes of morbidity and mortality amongst children under the age of five. This is a global issue and has been identified as an area requiring active research. The emphasis now is to identify and develop methods of predicting the likelihood of preterm birth. This paper uses physiological data from a group of patients in active labor. The dataset contains information about fetal heart rate (FHR) and maternal heart rate (MHR) for all patients and electrohysterogram (EHG) recordings for the measurement of uterine contractions. For the physiological data analysis and associated signal processing, we utilize deep wavelet scattering (DWS). This is an unsupervised decomposition and feature extraction method combining characteristics from deep learning convolutions, as well as the classical wavelet transform, to observe and investigate the extent to which active preterm labor can be accurately identified from an acquired physiological signal, the results of which were compared with the metaheuristic linear series decomposition learner (LSDL). Additional machine learning algorithms are tested on the acquired physiological data to allow for the identification of optimal model architecture for this specific physiological data
A Rainbow in Deep Network Black Boxes
We introduce rainbow networks as a probabilistic model of trained deep neural
networks. The model cascades random feature maps whose weight distributions are
learned. It assumes that dependencies between weights at different layers are
reduced to rotations which align the input activations. Neuron weights within a
layer are independent after this alignment. Their activations define kernels
which become deterministic in the infinite-width limit. This is verified
numerically for ResNets trained on the ImageNet dataset. We also show that the
learned weight distributions have low-rank covariances. Rainbow networks thus
alternate between linear dimension reductions and non-linear high-dimensional
embeddings with white random features. Gaussian rainbow networks are defined
with Gaussian weight distributions. These models are validated numerically on
image classification on the CIFAR-10 dataset, with wavelet scattering networks.
We further show that during training, SGD updates the weight covariances while
mostly preserving the Gaussian initialization.Comment: 56 pages, 10 figure
Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning
Artificial intelligence (AI) has brought tremendous impacts on biomedical
sciences from academic researches to clinical applications, such as in
biomarkers' detection and diagnosis, optimization of treatment, and
identification of new therapeutic targets in drug discovery. However, the
contemporary AI technologies, particularly deep machine learning (ML), severely
suffer from non-interpretability, which might uncontrollably lead to incorrect
predictions. Interpretability is particularly crucial to ML for clinical
diagnosis as the consumers must gain necessary sense of security and trust from
firm grounds or convincing interpretations. In this work, we propose a
tensor-network (TN)-ML method to reliably predict lung cancer patients and
their stages via screening Raman spectra data of Volatile organic compounds
(VOCs) in exhaled breath, which are generally suitable as biomarkers and are
considered to be an ideal way for non-invasive lung cancer screening. The
prediction of TN-ML is based on the mutual distances of the breath samples
mapped to the quantum Hilbert space. Thanks to the quantum probabilistic
interpretation, the certainty of the predictions can be quantitatively
characterized. The accuracy of the samples with high certainty is almost
100. The incorrectly-classified samples exhibit obviously lower certainty,
and thus can be decipherably identified as anomalies, which will be handled by
human experts to guarantee high reliability. Our work sheds light on shifting
the ``AI for biomedical sciences'' from the conventional non-interpretable ML
schemes to the interpretable human-ML interactive approaches, for the purpose
of high accuracy and reliability.Comment: 10 pages, 7 figure
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Computational and Imaging Methods for Studying Neuronal Populations during Behavior
One of the central questions in neuroscience is how the nervous system generates and regulates behavior. To understand the neural code for any behavior, an ideal experiment would entail (i) quantitatively defining that behavior, (ii) recording neuronal activity in relevant brain regions to identify the underlying neuronal circuits and eventually (iii) manipulating them to test their function. Novel methods in neuroscience have greatly advanced our abilities to conduct such experiments but are still insufficient. In this thesis, I developed methods for these three goals. In Chapter 2, I describe an automatic behavior identification and classification method for the cnidarian Hydra vulgaris using machine learning. In Chapter 3, I describe a fast volumetric two-photon microscope with dual-color laser excitation that can image in 3D the activity of populations of neurons from visual cortex of awake mice. In Chapter 4, I present a machine learning method that identifies cortical ensembles and pattern completion neurons in mouse visual cortex, using two-photon calcium imaging data. These methods advance current technologies, providing opportunities for new discoveries
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