Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE) - a study protocol for developing an automated intelligent decision support system for early fetal echocardiography.

Background: Congenital Heart Disease represents the most frequent fetal malformation. The lack of prenatal identification of congenital heart defects can have adverse consequences for the neonate, while a correct prenatal diagnosis of specific cardiac anomalies improves neonatal care neurologic and surgery outcomes. Sonographers perform prenatal diagnosis manually during the first or second-trimester scan, but the reported detection rates are low.

COVID-19 antibody level analysis with feature selection approach.

The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection.

Multi-Swarm Algorithm for Extreme Learning Machine Optimization.

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature.

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection.

We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets.

A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis.

Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly.

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals.

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified.

Immunohistochemical evaluation of tumor budding in colorectal cancer: an important parameter with prognostic value.

We analyzed 82 patients with colorectal cancer (CRC) [75 patients with mucinous adenocarcinoma (ADK) and seven patients with "signet ring cell" ADK] using multi-cytokeratin (CK) AE1∕AE3 immunohistochemical assay. In order to determine the mucinous nature of some of the lymph node metastases of the mucinous colorectal ADKs studied, Periodic Acid Schiff-Alcian Blue (PAS-AB) histochemical staining was used. The counting results were systematized in the following ranges: 0 budding areas; between 1-4 budding areas; between 5-9 budding areas; and =10 tumor budding (TB) areas.

Digital Assessment of Stained Breast Tissue Images for Comprehensive Tumor and Microenvironment Analysis.

Current histopathological diagnosis involves human expert interpretation of stained images for diagnosis. This process is prone to inter-observer variability, often leading to low concordance rates amongst pathologists across many types of tissues. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited.

Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations.

Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein. Towards this scope, two traditional deep learning architectures are designed in comparison: a long short-memory network and a temporal convolutional neural model.

Cancer diagnosis through a tandem of classifiers for digitized histopathological slides.

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results.

Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis.

This paper presents an automatic tool capable to learn from a patients data set with 24 medical indicators characterizing each sample and to subsequently use the acquired knowledge to differentiate between five degrees of liver fibrosis. The indicators represent clinical observations and the liver stiffness provided by the new, non-invasive procedure of Fibroscan. The proposed technique combines a hill climbing algorithm that selects subsets of important attributes for an accurate classification and a core represented by a cooperative coevolutionary classifier that builds rules for establishing the diagnosis for every new patient.

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

Objective: Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive.