This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.

MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

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A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Background In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on gastric tissues diseases, few intensive reviews are addressing this topic. Method We performed a systematic review related to applications of deep learning in gastric tissue disease analysis by digital histology, endoscopy and radiology images. Conclusions This review highlighted the high potential and shortcomings in deep learning research studies applied to gastric cancer, ulcer, gastritis and non-malignant diseases. Our results demonstrate the effectiveness of gastric tissue analysis by deep learning applications. Moreover, we also identified gaps of evaluation metrics, and image collection availability, therefore, impacting experimental reproducibility.

https://bmjopengastro.bmj.com/content/bmjgast/7/1/e000371.full.pdf

Deep learning in gastric tissue diseases: a systematic review.

Improving retail efficiency through sensing technologies

With mobile payments popular around the world, payers can conduct a payment anytime and anywhere. While providing great convenience, mobile payment also brings many payment security issues. This paper is the first comprehensive review of secure mobile payment. We classify the mobile payment into TPC (third-party payment company)-led mobile payment and Bank-led mobile payment, and based on this, summarize the system structure of mobile payment. Then we discuss the mobile payment security technology framework from Tokenization, PAN (bank card primary account number) binding, and Secure Payment Authentication, respectively. Besides, this paper introduces secure technologies(hardware and software) used in these procedures, discusses and analyzes the security issues that they have been encountered, summarise open issues, and proposes future development directions. In the end, we give the discussion and comparison of popular and representative mobile payment applications, including Alipay, Wechat Pay, Apple Pay, Samsung Pay, and Google Pay.

State of the Art: Secure Mobile Payment

Computer-aided diagnosis of cancer based on endoscopic image analysis is a promising area in the field of computer vision and machine learning. Convolutional neural networks are one of the most popular approaches in endoscopic image analysis. This paper presents the algorithm of pathology detection in endoscopic images of gastric lesions based on convolutional neural network. Training and testing of the algorithm was carried out on the NVIDIA DGX-1 supercomputer using endoscopic images from the test base, assembled together with the Yaroslavl Regional Cancer Hospital. As a result of experiments, the mAP metric was calculated and the value was 0.875, which is a high result for the task of object detection in images.

Deep Learning for Gastric Pathology Detection in Endoscopic Images

The real-time audience measurement system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and in-cloud data statistics analysis. The challenging part of such system is age estimation algorithm on the basis of machine learning methods. The face aging process is determined by different factors: genetic, lifestyle, expression and environment. That is why same age people can have quite different rates of facial aging. We propose a novel algorithm consisting of two stages: adaptive feature extraction based on local binary patterns and support vector machine classification. Experimental results on the FG-NET, MORPH and our own database are presented. Human perception ability in age estimation is studied using crowdsourcing which allows a comparison of the ability of machines and humans.

/pdf/age-estimation-from-face-images-challenging-problem-for-2p8fhatpqy.pdf

Age estimation from face images: challenging problem for audience measurement systems

Computer-aided diagnostics of cancer pathologies based on histological image segmentation is a promising area in the field of computer vision and machine learning. To date, the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them. The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net. Using this approach allows to get better results in the tasks of medical image segmentation. The developed algorithm based on neural network AlexNet was used for the creation of the automatic markup of the histological image database. The neural network algorithms were trained and tested on the NVIDIA DGX-1 supercomputer using histological images. The results of the research show that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.

Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images

This paper presents the new face verification algorithm based on deep convolutional neural network. The algorithm produces face feature vectors, distance between these vectors allows to determine whether images from the same class. Comparative experimental results are given for LFW test database and modern face recognition algorithms. ROC-curve and equal error rate are used to determine the accuracy of compared algorithms. Testing was carried out under the “image restricted” verification paradigm. With unsupervised learning, the algorithm can't have any access to the data class labels, the statistics of these labels, or the means of generating these labels. Proposed face verification algorithm is more accurate than other modern algorithms.

Face verification based on convolutional neural network and deep learning

Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. To test the applicability of the algorithm in the real world have been conducted research on a robustness. Robustness research shown that the algorithm based on the CEDT show that Gaussian noise, impulsive “salt-and-pepper” noise exert a strong influence on the algorithm (in the worst case decrease in the area under the ROC-curve of 21.2% with a decrease in PSNR metric to 17.99 dB). At the same time blurring, JPEG-compression and JPEG2000 algorithms distortion have little effect on the proposed face detection algorithm (reduction of the area under the ROC-curve by 3.5% while reducing PSNR metric to 21.58 dB).

Olga Stepanova

Papers

Deep Learning for Gastric Pathology Detection in Endoscopic Images

Age estimation from face images: challenging problem for audience measurement systems

Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images

Face verification based on convolutional neural network and deep learning

Face detection algorithm based on a cascade of ensembles of decision trees