AIMC Topic: Algorithms

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Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

Journal of biophotonics
The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimens...

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

Interdisciplinary sciences, computational life sciences
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce ...

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition.

Sensors (Basel, Switzerland)
Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER's critical problem with traditional local...

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BMC medical informatics and decision making
BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients' outcomes and data mining is such a powerf...

Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life.

BMC bioinformatics
BACKGROUND: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new ref...

High-content image generation for drug discovery using generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society
Immense amount of high-content image data generated in drug discovery screening requires computationally driven automated analysis. Emergence of advanced machine learning algorithms, like deep learning models, has transformed the interpretation and a...

Hybrid tensor decomposition in neural network compression.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks (DNNs) have enabled impressive breakthroughs in various artificial intelligence (AI) applications recently due to its capability of learning high-level features from big data. However, the current demand of DNNs for computational...

Using Chou's 5-steps rule to identify N-methyladenine sites by ensemble learning combined with multiple feature extraction methods.

Journal of biomolecular structure & dynamics
-methyladenine (m6A), a type of modification mostly affecting the downstream biological functions and determining the levels of gene expression, is mediated by the methylation of adenine in nucleic acids. It is also a key factor for influencing biolo...

Artificial intelligence in COVID-19 drug repurposing.

The Lancet. Digital health
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timel...

Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection.

Computers in biology and medicine
Class imbalance and the presence of irrelevant or redundant features in training data can pose serious challenges to the development of a classification framework. This paper proposes a framework for developing a Clinical Decision Support System (CDS...