The automatic classification of electrocardiogram
(ECG) signals has played an important role in cardiovascular
diseases diagnosis and prediction. Deep neural networks (DNNs),
particularly Convolutional Neural Networks (CNNs), have excelled
in a variety of intelligent tasks including biomedical and
health informatics. Most the existing approaches either partition
the ECG time series into a set of segments and apply 1D-CNNs or
divide the ECG signal into a set of spectrogram images and apply
2D-CNNs. These studies, however, suffer from the limitation that
temporal dependencies between 1D segments or 2D spectrograms
are not considered during network construction. Furthermore,
meta-data including gender and age has not been well studied
in these researches. To address those limitations, we propose a
multi-module Recurrent Convolutional Neural Networks (RCNNs)
consisting of both CNNs to learn spatial representation
and Recurrent Neural Networks (RNNs) to model the temporal
relationship. Our multi-module RCNNs architecture is designed
as an end-to-end deep framework with four modules: (i) timeseries
module by 1D RCNNs which extracts spatio-temporal
information of ECG time series; (ii) spectrogram module by
2D RCNNs which learns visual-temporal representation of ECG
spectrogram ; (iii) metadata module which vectorizes age and
gender information; (iv) fusion module which semantically fuses
the information from three above modules by a transformer
encoder. Ten-fold cross validation was used to evaluate the approach
on the MIT-BIH arrhythmia database (MIT-BIH) under
different network configurations. The experimental results have
proved that our proposed multi-module RCNNs with transformer
encoder achieves the state-of-the-art with 99.14% F1 score and
98.29% accuracy.
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Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.
more » « less- Award ID(s):
- 2028361
- NSF-PAR ID:
- 10148041
- Date Published:
- Journal Name:
- Proceedings of the 28th International Joint Conference on Artificial Intelligence
- Page Range / eLocation ID:
- 2894 to 2900
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24963/ijcai.2019/401
