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General General

WhiskEras: A New Algorithm for Accurate Whisker Tracking.

In Frontiers in cellular neuroscience ; h5-index 74.0

Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.

Betting Jan-Harm L F, Romano Vincenzo, Al-Ars Zaid, Bosman Laurens W J, Strydis Christos, De Zeeuw Chris I


Purkinje cell, algorithm, cerebellum, machine learning, mouse, object tracking, sensorimotor integration, whiskers

General General

Data Analysis of Covid-19 Pandemic and Short-Term Cumulative Case Forecasting Using Machine Learning Time Series Models.

In Chaos, solitons, and fractals

The Covid-19 pandemic is the most important health disaster that has surrounded the world for the past eight months. There is no clear date yet on when it will end. By now, more than 31 million people have been infected worldwide. Predicting the Covid-19 trend has become a challenging issue. In this study, data of COVID-19 between 20/01/2020 and 18/09/2020 for USA, Germany and Global was obtained from World Health Organization. Dataset consist of weekly confirmed cases and weekly cumulative confirmed cases for 35 weeks. Then the distribution of the data was examined using the most up-to-date Covid-19 weekly case data and its parameters were obtained according to the statistical distributions. Furthermore, time series prediction model using machine learning was proposed to obtain the curve of disease and forecast the epidemic tendency. Linear regression, multi-layer perceptron, random forest and support vector machines (SVM) machine learning methods were used. The performances of the methods were compared according to the RMSE, APE, MAPE metrics and it was seen that SVM achieved the best trend. According to estimates, the global pandemic will peak at the end of January 2021 and estimated approximately 80 million people will be cumulatively infected.

Ballı Serkan


Covid-19, Machine Learning, Multi-layer perceptron, Statistical distribution, Support vector machine

General General

Prediction of COVID-19 Confirmed Cases Combining Deep Learning Methods and Bayesian Optimization.

In Chaos, solitons, and fractals

COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions on the interventions that must be taken. In this study, we propose three hybrid approaches for forecasting COVID-19 time series methods based on combining three deep learning models such as multi-head attention, long short-term memory (LSTM), and convolutional neural network (CNN) with the Bayesian optimization algorithm. All models are designed based on the multiple-output forecasting strategy, which allows the forecasting of the multiple time points. The Bayesian optimization method automatically selects the best hyperparameters for each model and enhances forecasting performance. Using the publicly available epidemical data acquired from Johns Hopkins University's Coronavirus Resource Center, we conducted our experiments and evaluated the proposed models against the benchmark model. The results of experiments exhibit the superiority of the deep learning models over the benchmark model both for short-term forecasting and long-horizon forecasting. In particular, the mean SMAPE of the best deep learning model is 0.25 for the short-term forecasting (10 days ahead). Also, for long-horizon forecasting, the best deep learning model obtains the mean SMAPE of 2.59.

Abbasimehr Hossein, Paki Reza


Bayesian optimization, CNN, COVID-19, Deep learning, LSTM, Multi-head attention

General General

Analysis of news sentiments using natural language processing and deep learning.

In AI & society

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to "remember" previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios.

Vicari Mattia, Gaspari Mauro


Deep learning, Machine learning, NLP, Natural language processing, Sentiment analysis, Trading, Trading signals, Trading strategies

Radiology Radiology

Radiology indispensable for tracking COVID-19.

In Diagnostic and interventional imaging

With the rapid spread of COVID-19 worldwide, early detection and efficient isolation of suspected patients are especially important to prevent the transmission. Although nucleic acid testing of SARS-CoV-2 is still the gold standard for diagnosis, there are well-recognized early-detection problems including time-consuming in the diagnosis process, noticeable false-negative rate in the early stage and lacking nucleic acid testing kits in some areas. Therefore, effective and rational applications of imaging technologies are critical in aiding the screen and helping the diagnosis of suspected patients. Currently, chest computed tomography is recommended as the first-line imaging test for detecting COVID-19 pneumonia, which could allow not only early detection of the typical chest manifestations, but also timely estimation of the disease severity and therapeutic effects. In addition, other radiological methods including chest X-ray, magnetic resonance imaging, and positron emission computed tomography also show significant advantages in the detection of COVID-19 pneumonia. This review summarizes the applications of radiology and nuclear medicine in detecting and diagnosing COVID-19. It highlights the importance for these technologies to curb the rapid transmission during the pandemic, considering findings from special groups such as children and pregnant women.

Li Jingwen, Long Xi, Wang Xinyi, Fang Fang, Lv Xuefei, Zhang Dandan, Sun Yu, Hu Shaoping, Lin Zhicheng, Xiong Nian


Artificial intelligence, COVID-19, Magnetic resonance imaging, Positron emission tomography computed tomography., Tomography, X-ray computed

Surgery Surgery

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

In The Journal of arthroplasty ; h5-index 65.0

BACKGROUND : The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidity, and further economic burden. Because few arthroplasty experts can confidently classify implants using plain radiographs, automated image processing using deep learning for implant identification may offer an opportunity to improve the value of care rendered.

METHODS : We trained, validated, and externally tested a deep-learning system to classify total hip arthroplasty and hip resurfacing arthroplasty femoral implants as one of 18 different manufacturer models from 1972 retrospectively collected anterior-posterior (AP) plain radiographs from 4 sites in one quaternary referral health system. From these radiographs, 1559 were used for training, 207 for validation, and 206 for external testing. Performance was evaluated by calculating the area under the receiver-operating characteristic curve, sensitivity, specificity, and accuracy, as compared with a reference standard of implant model from operative reports with implant serial numbers.

RESULTS : The training and validation data sets from 1715 patients and 1766 AP radiographs included 18 different femoral components across four leading implant manufacturers and 10 fellowship-trained arthroplasty surgeons. After 1000 training epochs by the deep-learning system, the system discriminated 18 implant models with an area under the receiver-operating characteristic curve of 0.999, accuracy of 99.6%, sensitivity of 94.3%, and specificity of 99.8% in the external-testing data set of 206 AP radiographs.

CONCLUSIONS : A deep-learning system using AP plain radiographs accurately differentiated among 18 hip arthroplasty models from four industry leading manufacturers.

Karnuta Jaret M, Haeberle Heather S, Luu Bryan C, Roth Alexander L, Molloy Robert M, Nystrom Lukas M, Piuzzi Nicolas S, Schaffer Jonathan L, Chen Antonia F, Iorio Richard, Krebs Viktor E, Ramkumar Prem N


artificial intelligence, implant identification, machine learning, revision arthroplasty, total hip arthroplasty