327,866 research outputs found
A deep learning approach to diabetic blood glucose prediction
We consider the question of 30-minute prediction of blood glucose levels
measured by continuous glucose monitoring devices, using clinical data. While
most studies of this nature deal with one patient at a time, we take a certain
percentage of patients in the data set as training data, and test on the
remainder of the patients; i.e., the machine need not re-calibrate on the new
patients in the data set. We demonstrate how deep learning can outperform
shallow networks in this example. One novelty is to demonstrate how a
parsimonious deep representation can be constructed using domain knowledge
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown
promising results. However, compared to other approaches, their power is
strongly linked to the dataset size. In this study, we evaluate
3D-convolutional neural networks (CNNs) and classical regression methods with
hand-crafted features for survival time regression of patients with high grade
brain tumors. The tested CNNs for regression showed promising but unstable
results. The best performing deep learning approach reached an accuracy of
51.5% on held-out samples of the training set. All tested deep learning
experiments were outperformed by a Support Vector Classifier (SVC) using 30
radiomic features. The investigated features included intensity, shape,
location and deep features. The submitted method to the BraTS 2018 survival
prediction challenge is an ensemble of SVCs, which reached a cross-validated
accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set,
and 42.9% on the testing set. The results suggest that more training data is
necessary for a stable performance of a CNN model for direct regression from
magnetic resonance images, and that non-imaging clinical patient information is
crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation
(BraTS) Challenge 2018, survival prediction tas
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks
Protein secondary structure prediction is an important problem in
bioinformatics. Inspired by the recent successes of deep neural networks, in
this paper, we propose an end-to-end deep network that predicts protein
secondary structures from integrated local and global contextual features. Our
deep architecture leverages convolutional neural networks with different kernel
sizes to extract multiscale local contextual features. In addition, considering
long-range dependencies existing in amino acid sequences, we set up a
bidirectional neural network consisting of gated recurrent unit to capture
global contextual features. Furthermore, multi-task learning is utilized to
predict secondary structure labels and amino-acid solvent accessibility
simultaneously. Our proposed deep network demonstrates its effectiveness by
achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public
benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11.
Our model and results are publicly available.Comment: 8 pages, 3 figures, Accepted by International Joint Conferences on
Artificial Intelligence (IJCAI
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Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping
Signal2Image Modules in Deep Neural Networks for EEG Classification
Deep learning has revolutionized computer vision utilizing the increased
availability of big data and the power of parallel computational units such as
graphical processing units. The vast majority of deep learning research is
conducted using images as training data, however the biomedical domain is rich
in physiological signals that are used for diagnosis and prediction problems.
It is still an open research question how to best utilize signals to train deep
neural networks.
In this paper we define the term Signal2Image (S2Is) as trainable or
non-trainable prefix modules that convert signals, such as
Electroencephalography (EEG), to image-like representations making them
suitable for training image-based deep neural networks defined as `base
models'. We compare the accuracy and time performance of four S2Is (`signal as
image', spectrogram, one and two layer Convolutional Neural Networks (CNNs))
combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet)
along with the depth-wise and 1D variations of the latter. We also provide
empirical evidence that the one layer CNN S2I performs better in eleven out of
fifteen tested models than non-trainable S2Is for classifying EEG signals and
we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201
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