AIMC Topic: Algorithms

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Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy.

Computers in biology and medicine
BACKGROUND: In recent years, numerous fusion algorithms have been proposed for multimodal medical images. The Laplacian pyramid is one type of multiscale fusion method. Although the pyramid-based fusion algorithm can fuse images well, it has the disa...

Localization and recognition of leukocytes in peripheral blood: A deep learning approach.

Computers in biology and medicine
Automatic recognition and classification of leukocytes helps medical practitioners to diagnose various blood-related diseases by analysing their percentages. Different researchers have come up with different algorithms that use traditional learning f...

Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Scientific reports
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we perf...

BrainNET: Inference of Brain Network Topology Using Machine Learning.

Brain connectivity
To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. B...

Use of artificial intelligence for tailored routine urine analyses.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
OBJECTIVES: Urine is the most common material tested in clinical microbiology laboratories. Automated analysis is already performed, permitting quicker results and decreasing the laboratory technologist's (LT) workload. These automatic systems have i...

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.

Medical image analysis
Manual delineation of anatomy on existing images is the basis of developing deep learning algorithms for medical image segmentation. However, manual segmentation is tedious. It is also expensive because clinician effort is necessary to ensure correct...

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.

BMC bioinformatics
BACKGROUND: Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algo...

Application of BERT to Enable Gene Classification Based on Clinical Evidence.

BioMed research international
The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of gene...

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

PloS one
To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this ...

Neural Network Potential Energy Surfaces for Small Molecules and Reactions.

Chemical reviews
We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational...