AIMC Topic: Neural Networks, Computer

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Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Scientific reports
Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackl...

Semantic segmentation of COVID-19 lesions with a multiscale dilated convolutional network.

Scientific reports
Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 le...

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Nature communications
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic...

Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior.

Nature communications
The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in "conjunction hu...

Deep learning for prediction of population health costs.

BMC medical informatics and decision making
BACKGROUND: Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing.

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.

IEEE transactions on neural networks and learning systems
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data...

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

Computational and mathematical methods in medicine
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. T...

SPP-CPI: Predicting Compound-Protein Interactions Based On Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying interactions between compound and protein is a substantial part of the drug discovery process. Accurate prediction of interaction relationships can greatly reduce the time of drug development. The uniqueness of our method lies in three as...

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and...

Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography.

IEEE/ACM transactions on computational biology and bioinformatics
Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecul...