AIMC Topic: Deep Learning

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Multi-scale full spike pattern for semantic segmentation.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs), as the brain-inspired neural networks, encode information in spatio-temporal dynamics. They have the potential to serve as low-power alternatives to artificial neural networks (ANNs) due to their sparse and event-drive...

From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real ti...

Automatic classification of dog barking using deep learning.

Behavioural processes
Barking and other dog vocalizations have acoustic properties related to emotions, physiological reactions, attitudes, or some particular internal states. In the field of intelligent audio analysis, researchers use methods based on signal processing a...

COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.

Sensors (Basel, Switzerland)
Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and ava...

TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences.

BMC bioinformatics
BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for i...

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BMC bioinformatics
BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online ...

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

Journal of thrombosis and haemostasis : JTH
BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for ...

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

Medical physics
BACKGROUND: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relat...

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock.

Computational biology and chemistry
A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock speci...