Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

How, When, and Why: High-Density Mapping of Atrial Fibrillation.

In Cardiac electrophysiology clinics

High-density (HD) mapping presents opportunities to enhance delineation of atrial fibrillation (AF) substrate, improve efficiency of the mapping procedure without sacrificing safety, and afford new mechanistic insights regarding AF. Innovations in hardware, software algorithms, and development of novel multielectrode catheters have allowed HD mapping to be feasible and reliable. Patients to particularly benefit from this technology are those with paroxysmal AF in setting of preexisting atrial scar, persistent AF, and AF in the setting of complex congenital heart disease. The future will bring refinements in automated HD mapping including evolution of noncontact methodologies and artificial intelligence to supplant current techniques.

Kodali Santhisri, Santangeli Pasquale


Atrial fibrillation, Catheter ablation, High-density mapping

Public Health Public Health

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : COVID-19 has affected more than 200 countries and territories worldwide. It poses an extraordinary challenge for public health systems, because screening and surveillance capacity-especially during the beginning of the outbreak-is often severely limited, fueling the outbreak as many patients unknowingly infect others.

OBJECTIVE : We present an effort to collect and analyze COVID-19 related posts on the popular Twitter-like social media site in China, Weibo. To our knowledge, this infoveillance study employs the largest, most comprehensive and fine-grained social media data to date to predict COVID-19 case counts in mainland China.

METHODS : We built a Weibo user pool of 250 million, approximately half of the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19 related posts from our user pool, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China.

RESULTS : We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline.

CONCLUSIONS : Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understandings of information sharing behaviors is a promising approach to identifying true disease signals and improving the effectiveness of infoveillance.


Shen Cuihua, Chen Anfan, Luo Chen, Zhang Jingwen, Feng Bo, Liao Wang


Pathology Pathology

Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation

ArXiv Preprint

In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of heterogenous multi-phase data with possibly new clinical scenarios and pathologies. To do this, we propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. We also introduce co-heterogeneous training, which is a novel integration of co-training and hetero modality learning. We have evaluated CHASe using a clinically comprehensive and challenging dataset of multi-phase computed tomography (CT) imaging studies (1147 patients and 4577 3D volumes). Compared to previous state-of-the-art baselines, CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2\% \sim 9.4\%$, depending on the phase combinations: e.g., from $84.6\%$ to $94.0\%$ on non-contrast CTs.

Ashwin Raju, Chi-Tung Cheng, Yunakai Huo, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHuang Liao, Adam P Harrison


General General

Generalized norm for existence, uniqueness and stability of Hopfield neural networks with discrete and distributed delays.

In Neural networks : the official journal of the International Neural Network Society

In this paper, the existence, uniqueness and stability criteria of solutions for Hopfield neural networks with discrete and distributed delays (DDD HNNs) are investigated by the definitions of three kinds of generalized norm (ξ-norm). A general DDD HNN model is firstly introduced, where the discrete delays τpq(t) are asynchronous time-varying delays. Then, {ξ,1}-norm, {ξ,2}-norm and {ξ,∞}-norm are successively used to derive the existence, uniqueness and stability criteria of solutions for the DDD HNNs. In the proof of theorems, special functions and assumptions are given to deal with discrete and distributed delays. Furthermore, a corollary is concluded for the existence and stability criteria of solutions. The methods given in this paper can also be used to study the synchronization and μ-stability of different DDD NNs. Finally, two numerical examples and their simulation figures are given to illustrate the effectiveness of these results.

Wang Huamin, Wei Guoliang, Wen Shiping, Huang Tingwen


-norm, Discrete-distributed delays, Exponential stability, Hopfield neural networks

General General

Uni-image: Universal image construction for robust neural model.

In Neural networks : the official journal of the International Neural Network Society

Deep neural networks have shown high performance in prediction, but they are defenseless when they predict on adversarial examples which are generated by adversarial attack techniques. In image classification, those attack techniques usually perturb the pixel of an image to fool the deep neural networks. To improve the robustness of the neural networks, many researchers have introduced several defense techniques against those attack techniques. To the best of our knowledge, adversarial training is one of the most effective defense techniques against the adversarial examples. However, the defense technique could fail against a semantic adversarial image that performs arbitrary perturbation to fool the neural networks, where the modified image semantically represents the same object as the original image. Against this background, we propose a novel defense technique, Uni-Image Procedure (UIP) method. UIP generates a universal-image (uni-image) from a given image, which can be a clean image or a perturbed image by some attacks. The generated uni-image preserves its own characteristics (i.e. color) regardless of the transformations of the original image. Note that those transformations include inverting the pixel value of an image, modifying the saturation, hue, and value of an image, etc. Our experimental results using several benchmark datasets show that our method not only defends well known adversarial attacks and semantic adversarial attack but also boosts the robustness of the neural network.

Ho Jiacang, Lee Byung-Gook, Kang Dae-Ki


Adversarial machine learning, Defense technique, Image classification, Semantic adversarial example, Uni-Image Procedure

General General

Active source localization in wave guides based on machine learning.

In Ultrasonics

In the present work, an active source localization strategy is proposed. The presence of active sources in a waveguide can have several reasons, such as crack initiation or internal friction. In this study, the active source is represented by an impact event. A steel ball is dropped on an aluminum plate at different positions. Elastic waves are excited and will propagate through the plate. The wave response is acquired by a piezoelectric sensor network, which is attached to the plate. After performing numerical and physical experiments, enough data are collected in order to train an artificial neural network and a support vector machine. Those machine learning algorithms will predict the impact position based on the wave response of each sensor, while only numerical data from the finite element simulations are used to train both methods. After the training process is completed, the algorithms are applied to experimental data. A good agreement between reference and predicted results proves that the wave responses at the piezoelectric transducers contain sufficient information in order to localize the impact position precisely.

Hesser Daniel Frank, Kocur Georg Karl, Markert Bernd


Artificial neural network, Computational intelligence, Guided elastic waves, Impact dynamics, Structural health monitoring