Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography scans. Design - Development and validation of deep learning models for classification and feature segmentation. Methods - A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the ROC curve (AUC), kappa, accuracy and intraclass correlation coefficient (ICC). Results - The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification model). The mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance. Conclusions - The models achieved high classification and segmentation performance, similar to human performance. Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.

Deepfakes and manipulated media are becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at combating Deepfakes using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of identifying and detecting fake media using visual artifacts, we propose to proactively embed a semi-fragile watermark into a real image so that we can prove its authenticity when needed. Our watermarking framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations such as image compression, scaling, saturation, contrast adjustments etc. This allows images shared over the internet to retain the verifiable watermark as long as face-swapping or any other Deepfake modification technique is not applied. We demonstrate that FaceSigns can embed a 128 bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen Deepfake manipulations are applied. For a set of unseen benign and Deepfake manipulations studied in our work, FaceSigns can reliably detect manipulated content with an AUC score of 0.996 which is significantly higher than prior image watermarking and steganography techniques.

In logistic regression it can output probability of classifying new label as positive(class 1) and if probability is above the threshold point then you predict positive class else negative class. For example if threshold is 20% more labels will be classified as positive leading to increase in recall and decrease in precision. One trick that I started using is changing all the datatypes appropriately to reduce memory, this not only allow your computer to carry larger dataframes however it having right datatypes make computation more efficient. To plot ROC curve we need to measure TPR and FPR at each threshold point. In order to make predictions with different threshold, instead of predicting labels we need to obtain probabilities of new data belonging to each label which can be obtained by predict_proba() .

Let's read in all the data we have. Since all the region data share a primary key date, we can connect them with concat() in pandas and set the keys as the region names. I don't want the regions as the index, so we can reset the index and then rename some columns to get the data in the right shape. Let's first visualize our target class. It appears we have in our hands an imbalanced class, as the N label is dominating the rest of the classes.

John Quackenbush was frustrated with Google. It was January 2020, and a team led by researchers from Google Health had just published a study in Nature about an artificial intelligence (AI) system they had developed to analyze mammograms for signs of breast cancer. The system didn't just work, according to the study, it worked exceptionally well. When the team fed it two large sets of images to analyze--one from the UK and one from the U.S.--it reduced false positives by 1.2 and 5.7 percent and false negatives by 2.7 and 9.4 percent compared with the original determinations made by medical professionals. In a separate test that pitted the AI system against six board-certified radiologists in analyzing nearly 500 mammograms, the algorithm outperformed each of the specialists. The authors concluded that the system was "capable of surpassing human experts in breast cancer prediction" and ready for clinical trials. An avalanche of buzzy headlines soon followed. "Google AI system can beat doctors at detecting breast cancer," a CNN story declared.

Qiuchong Chen,1,* Yixue Zhang,1,* Mengjun Zhang,1 Ziying Li,1 Jindong Liu1,2 1Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China; 2Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China *These authors contributed equally to this work Correspondence: Jindong Liu, Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road West, Quanshan District, Xuzhou, Jiangsu, 221000, People's Republic of China, Email [email protected] Objective: There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients' early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods: We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n 799) and test (20%;n 201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results: In predicting AKI, nine ML algorithms posted AUC of 0.656– 1.000 in the training cohort, with the randomforest standing out and AUC of 0.674– 0.821 in the test cohort, with the logistic regression model standing out.

Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios include but are not limited to: elderly people living alone, people suffering from dementia, home quarantine. The majority of published papers focus on either pressure / door sensors or cameras in order to detect outing events. Although the aforementioned approaches provide solid results, they are intrusive and require modifications for sensor placement. In our work, appliance electrical use is investigated as a means for detecting the presence or absence of residents. The energy use is the result of power disaggregation, a non intrusive / non invasive sensing method. Since a dataset providing energy data and ground truth for home absence is not available, artificial outing events were introduced on the UK-DALE dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several machine learning algorithms were evaluated using the generated dataset. Benchmark results have shown that home absence detection using appliance power consumption is feasible.

In part 1, I compared a few model evaluation techniques that fall under the umbrella of'general statistical tools and tests'. Here in Part 2 I compare three of the more popular model evaluation techniques for classification and clustering: confusion matrix, gain and lift chart, and ROC curve. That said, you'll want to choose a method that gives you the answers you need for the particular field you're in. For example, while a confusion matrix can be a great tool for comparing models, it isn't much good for marketing decisions (where the gain and lift chart would be a better choice). Other less popular (but still valid) tools include the K-S chart and Gini Coefficient.

Large rotating machines, e.g., compressors, steam turbines, gas turbines, are critical equipment in many process industries such as energy, chemical, and power generation. Due to high rotating speed and tremendous momentum of the rotor, the centrifugal force may lead to flying apart of the rotor parts, which brings a great threat to the operation safety. Early detection and prediction of potential failures could prevent the catastrophic plant downtime and economic loss. In this paper, we divide the operational states of a rotating machine into normal, risky, and high-risk ones based on the time to the moment of failure. Then a cascade classifying algorithm is proposed to predict the states in two steps, first we judge whether the machine is in normal or abnormal condition; for time periods which are predicted as abnormal we further classify them into risky or high-risk states. Moreover, traditional classification model evaluation metrics, such as confusion matrix, true-false accuracy, are static and neglect the online prediction dynamics and uneven wrong-prediction prices. An Online Prediction Ability Index (OPAI) is proposed to select prediction models with consistent online predictions and smaller close-to-downtime prediction errors. Real-world data sets and computational experiments are used to verify the effectiveness of proposed methods.

Quantum computing continues to advance at a rapid pace, but one challenge that holds the field back is mitigating the noise that plagues quantum machines. This leads to much higher error rates compared to classical computers. This noise is often caused by imperfect control signals, interference from the environment, and unwanted interactions between qubits, which are the building blocks of a quantum computer. Performing computations on a quantum computer involves a "quantum circuit," which is a series of operations called quantum gates. These quantum gates, which are mapped to the individual qubits, change the quantum states of certain qubits, which then perform the calculations to solve a problem.