552 research outputs found
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 430 15-minute video clips, where actions are localized in space and
time, resulting in 1.58M action labels with multiple labels per person
occurring frequently. The key characteristics of our dataset are: (1) the
definition of atomic visual actions, rather than composite actions; (2) precise
spatio-temporal annotations with possibly multiple annotations for each person;
(3) exhaustive annotation of these atomic actions over 15-minute video clips;
(4) people temporally linked across consecutive segments; and (5) using movies
to gather a varied set of action representations. This departs from existing
datasets for spatio-temporal action recognition, which typically provide sparse
annotations for composite actions in short video clips. We will release the
dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic
difficulty of action recognition. To benchmark this, we present a novel
approach for action localization that builds upon the current state-of-the-art
methods, and demonstrates better performance on JHMDB and UCF101-24 categories.
While setting a new state of the art on existing datasets, the overall results
on AVA are low at 15.6% mAP, underscoring the need for developing new
approaches for video understanding.Comment: To appear in CVPR 2018. Check dataset page
https://research.google.com/ava/ for detail
A Graph-Temporal fused dual-input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61922062, and 61873181.Peer reviewedPostprin
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as established algorithms designed for this purpose. In decoding
EEG pathology, both ConvNets reached substantially better accuracies (about 6%
better, ~85% vs. ~79%) than the only published result for this dataset, and
were still better when using only 1 minute of each recording for training and
only six seconds of each recording for testing. We used automated methods to
optimize architectural hyperparameters and found intriguingly different ConvNet
architectures, e.g., with max pooling as the only nonlinearity. Visualizations
of the ConvNet decoding behavior showed that they used spectral power changes
in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
other features, consistent with expectations derived from spectral analysis of
the EEG data and from the textual medical reports. Analysis of the textual
medical reports also highlighted the potential for accuracy increases by
integrating contextual information, such as the age of subjects. In summary,
the ConvNets and visualization techniques used in this study constitute a next
step towards clinically useful automated EEG diagnosis and establish a new
baseline for future work on this topic.Comment: Published at IEEE SPMB 2017 https://www.ieeespmb.org/2017
Visualising Convolutional Neural Network Decisions in Automatic Sleep Scoring
Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework for automated sleep classification, however, the lack of interpretability of its results has hampered CNN's further use in medicine. In this study, we train a CNN using as input Continuous Wavelet transformed EEG, EOG and EMG recordings from a publicly available dataset. The network achieved a 10-fold cross-validation Cohen's Kappa score of . Further, we provide insights on how this network classifies individual epochs of sleep using Guided Gradient-weighted Class Activation Maps (Guided Grad-CAM). The proposed approach is able to produce fine-grained activation maps on time-frequency domain for each signal providing a useful tool for identifying relevant features in CNNs
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Neural networks are becoming more and more popular for the analysis of
physiological time-series. The most successful deep learning systems in this
domain combine convolutional and recurrent layers to extract useful features to
model temporal relations. Unfortunately, these recurrent models are difficult
to tune and optimize. In our experience, they often require task-specific
modifications, which makes them challenging to use for non-experts. We propose
U-Time, a fully feed-forward deep learning approach to physiological time
series segmentation developed for the analysis of sleep data. U-Time is a
temporal fully convolutional network based on the U-Net architecture that was
originally proposed for image segmentation. U-Time maps sequential inputs of
arbitrary length to sequences of class labels on a freely chosen temporal
scale. This is done by implicitly classifying every individual time-point of
the input signal and aggregating these classifications over fixed intervals to
form the final predictions. We evaluated U-Time for sleep stage classification
on a large collection of sleep electroencephalography (EEG) datasets. In all
cases, we found that U-Time reaches or outperforms current state-of-the-art
deep learning models while being much more robust in the training process and
without requiring architecture or hyperparameter adaptation across tasks.Comment: To appear in Advances in Neural Information Processing Systems
(NeurIPS), 201
Data-efficient Deep Learning Approach for Single-Channel EEG-Based Sleep Stage Classification with Model Interpretability
Sleep, a fundamental physiological process, occupies a significant portion of
our lives. Accurate classification of sleep stages serves as a crucial tool for
evaluating sleep quality and identifying probable sleep disorders. Our work
introduces a novel methodology that utilizes a SE-Resnet-Bi-LSTM architecture
to classify sleep into five separate stages. The classification process is
based on the analysis of single-channel electroencephalograms (EEGs). The
suggested framework consists of two fundamental elements: a feature extractor
that utilizes SE-ResNet, and a temporal context encoder that uses stacks of
Bi-LSTM units. The effectiveness of our approach is substantiated by thorough
assessments conducted on three different datasets, namely SleepEDF-20,
SleepEDF-78, and SHHS. The proposed methodology achieves significant model
performance, with Macro-F1 scores of 82.5, 78.9, and 81.9 for the respective
datasets. We employ 1D-GradCAM visualization as a methodology to elucidate the
decision-making process inherent in our model in the realm of sleep stage
classification. This visualization method not only provides valuable insights
into the model's classification rationale but also aligns its outcomes with the
annotations made by sleep experts. One notable feature of our research lies in
the incorporation of an efficient training approach, which adeptly upholds the
model's resilience in terms of performance. The experimental evaluations
provide a comprehensive evaluation of the effectiveness of our proposed model
in comparison to the existing approaches, highlighting its potential for
practical applications
U-Sleep's resilience to AASM guidelines
AASM guidelines are the result of decades of efforts aiming at standardizing
sleep scoring procedure, with the final goal of sharing a worldwide common
methodology. The guidelines cover several aspects from the technical/digital
specifications,e.g., recommended EEG derivations, to detailed sleep scoring
rules accordingly to age. Automated sleep scoring systems have always largely
exploited the standards as fundamental guidelines. In this context, deep
learning has demonstrated better performance compared to classical machine
learning. Our present work shows that a deep learning based sleep scoring
algorithm may not need to fully exploit the clinical knowledge or to strictly
adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a
state-of-the-art sleep scoring algorithm, can be strong enough to solve the
scoring task even using clinically non-recommended or non-conventional
derivations, and with no need to exploit information about the chronological
age of the subjects. We finally strengthen a well-known finding that using data
from multiple data centers always results in a better performing model compared
with training on a single cohort. Indeed, we show that this latter statement is
still valid even by increasing the size and the heterogeneity of the single
data cohort. In all our experiments we used 28528 polysomnography studies from
13 different clinical studies
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