Continuous long-term monitoring of electrocardiography (ECG) signals is
crucial for the early detection of cardiac abnormalities such as arrhythmia.
Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often
suffer from severe artifacts such as baseline wander, signal cuts, motion
artifacts, variations on QRS amplitude, noise, and other interferences.
Usually, a set of such artifacts occur on the same ECG signal with varying
severity and duration, and this makes an accurate diagnosis by machines or
medical doctors extremely difficult. Despite numerous studies that have
attempted ECG denoising, they naturally fail to restore the actual ECG signal
corrupted with such artifacts due to their simple and naive noise model. In
this study, we propose a novel approach for blind ECG restoration using
cycle-consistent generative adversarial networks (Cycle-GANs) where the quality
of the signal can be improved to a clinical level ECG regardless of the type
and severity of the artifacts corrupting the signal. To further boost the
restoration performance, we propose 1D operational Cycle-GANs with the
generative neuron model. The proposed approach has been evaluated extensively
using one of the largest benchmark ECG datasets from the China Physiological
Signal Challenge (CPSC-2020) with more than one million beats. Besides the
quantitative and qualitative evaluations, a group of cardiologists performed
medical evaluations to validate the quality and usability of the restored ECG,
especially for an accurate arrhythmia diagnosis.

本文提出了一个新方法，使用 Cycle-GAN 来盲目去噪 ECG 信号，从而提高信号质量，无论信号受到何种类型和严重程度的干扰，使其恢复成临床水平的 ECG。该方法在 CPSC-2020 ECG 数据集上经过了量化和定性评估，并得到了心脏病专家的验证。

基于循环生成对抗网络的盲源心电图恢复

Blind ECG Restoration by Operational Cycle-GANs

Deep networks are now ubiquitous in large-scale multi-center imaging studies.
However, the direct aggregation of images across sites is contraindicated for
downstream statistical and deep learning-based image analysis due to
inconsistent contrast, resolution, and noise. To this end, in the absence of
paired data, variations of Cycle-consistent Generative Adversarial Networks
have been used to harmonize image sets between a source and target domain.
Importantly, these methods are prone to instability, contrast inversion,
intractable manipulation of pathology, and steganographic mappings which limit
their reliable adoption in real-world medical imaging. In this work, based on
an underlying assumption that morphological shape is consistent across imaging
sites, we propose a segmentation-renormalized image translation framework to
reduce inter-scanner heterogeneity while preserving anatomical layout. We
replace the affine transformations used in the normalization layers within
generative networks with trainable scale and shift parameters conditioned on
jointly learned anatomical segmentation embeddings to modulate features at
every level of translation. We evaluate our methodologies against recent
baselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on
datasets with and without lesions. Segmentation-renormalization for translation
GANs yields superior image harmonization as quantified by Inception distances,
demonstrates improved downstream utility via post-hoc segmentation accuracy,
and improved robustness to translation perturbation and self-adversarial
attacks.

本文提出了一种基于分割归一化图像转换框架的方法，通过用可训练的比例和位移参数代替归一化层中的仿射变换，调制每个级别的特征，从而减少各种成像设备之间的异构性，同时保留解剖结构布局，并在多种成像模态（T1w MRI，FLAIR MRI 和 OCT）的数据集上进行评估。

基于分割重标准化的深度特征调制技术，用于非配对图像协调

Segmentation-Renormalized Deep Feature Modulation for Unpaired Image  Harmonization

We present an image translation approach to generate augmented data for
mitigating data imbalances in a dataset of histopathology images of colorectal
polyps, adenomatous tumors that can lead to colorectal cancer if left
untreated. By applying cycle-consistent generative adversarial networks
(CycleGANs) to a source domain of normal colonic mucosa images, we generate
synthetic colorectal polyp images that belong to diagnostically less common
polyp classes. Generated images maintain the general structure of their source
image but exhibit adenomatous features that can be enhanced with our proposed
filtration module, called Path-Rank-Filter. We evaluate the quality of
generated images through Turing tests with four gastrointestinal pathologists,
finding that at least two of the four pathologists could not identify generated
images at a statistically significant level. Finally, we demonstrate that using
CycleGAN-generated images to augment training data improves the AUC of a
convolutional neural network for detecting sessile serrated adenomas by over
10%, suggesting that our approach might warrant further research for other
histopathology image classification tasks.

使用循环一致生成对抗网络（CycleGANs）进行图像转换并生成人工合成结肠息肉图像，利用生成图像来增强数据集的训练效果，提高卷积神经网络（CNN）检测息肉的 AUC 值，该方法为解决组织病理学图像分类任务中数据不平衡问题提供了一种有效解决方案。