3 research outputs found
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for
heart diseases. Many studies have devised ECG analysis models (e.g.,
classifiers) to assist diagnosis. As an upstream task, researches have built
generative models to synthesize ECG data, which are beneficial to providing
training samples, privacy protection, and annotation reduction. However,
previous generative methods for ECG often neither synthesized multi-view data,
nor dealt with heart disease conditions. In this paper, we propose a novel
disease-aware generative adversarial network for multi-view ECG synthesis
called ME-GAN, which attains panoptic electrocardio representations conditioned
on heart diseases and projects the representations onto multiple standard views
to yield ECG signals. Since ECG manifestations of heart diseases are often
localized in specific waveforms, we propose a new "mixup normalization" to
inject disease information precisely into suitable locations. In addition, we
propose a view discriminator to revert disordered ECG views into a
pre-determined order, supervising the generator to obtain ECG representing
correct view characteristics. Besides, a new metric, rFID, is presented to
assess the quality of the synthesized ECG signals. Comprehensive experiments
verify that our ME-GAN performs well on multi-view ECG signal synthesis with
trusty morbid manifestations
Fetal Brain Tissue Annotation and Segmentation Challenge Results
In-utero fetal MRI is emerging as an important tool in the diagnosis and
analysis of the developing human brain. Automatic segmentation of the
developing fetal brain is a vital step in the quantitative analysis of prenatal
neurodevelopment both in the research and clinical context. However, manual
segmentation of cerebral structures is time-consuming and prone to error and
inter-observer variability. Therefore, we organized the Fetal Tissue Annotation
(FeTA) Challenge in 2021 in order to encourage the development of automatic
segmentation algorithms on an international level. The challenge utilized FeTA
Dataset, an open dataset of fetal brain MRI reconstructions segmented into
seven different tissues (external cerebrospinal fluid, grey matter, white
matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international
teams participated in this challenge, submitting a total of 21 algorithms for
evaluation. In this paper, we provide a detailed analysis of the results from
both a technical and clinical perspective. All participants relied on deep
learning methods, mainly U-Nets, with some variability present in the network
architecture, optimization, and image pre- and post-processing. The majority of
teams used existing medical imaging deep learning frameworks. The main
differences between the submissions were the fine tuning done during training,
and the specific pre- and post-processing steps performed. The challenge
results showed that almost all submissions performed similarly. Four of the top
five teams used ensemble learning methods. However, one team's algorithm
performed significantly superior to the other submissions, and consisted of an
asymmetrical U-Net network architecture. This paper provides a first of its
kind benchmark for future automatic multi-tissue segmentation algorithms for
the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript
submitte
DANets: Deep Abstract Networks for Tabular Data Classification and Regression
Tabular data are ubiquitous in real world applications. Although many
commonly-used neural components (e.g., convolution) and extensible neural
networks (e.g., ResNet) have been developed by the machine learning community,
few of them were effective for tabular data and few designs were adequately
tailored for tabular data structures. In this paper, we propose a novel and
flexible neural component for tabular data, called Abstract Layer (AbstLay),
which learns to explicitly group correlative input features and generate
higher-level features for semantics abstraction. Also, we design a structure
re-parameterization method to compress the learned AbstLay, thus reducing the
computational complexity by a clear margin in the reference phase. A special
basic block is built using AbstLays, and we construct a family of Deep Abstract
Networks (DANets) for tabular data classification and regression by stacking
such blocks. In DANets, a special shortcut path is introduced to fetch
information from raw tabular features, assisting feature interactions across
different levels. Comprehensive experiments on seven real-world tabular
datasets show that our AbstLay and DANets are effective for tabular data
classification and regression, and the computational complexity is superior to
competitive methods. Besides, we evaluate the performance gains of DANet as it
goes deep, verifying the extendibility of our method. Our code is available at
https://github.com/WhatAShot/DANet.Comment: @inproceedings{danets, title={DANets: Deep Abstract Networks for
Tabular Data Classification and Regression}, author={Chen, Jintai and Liao,
Kuanlun and Wan, Yao and Chen, Danny Z and Wu, Jian}, booktitle={AAAI},
year={2022}