AIMC Topic: Deep Learning

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An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

Computers in biology and medicine
BACKGROUND: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these...

Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning.

Journal of chemical information and modeling
Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (...

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

Microbiome
BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identific...

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.

BMC bioinformatics
BACKGROUND: The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretabili...

Application of deep learning in isolated tooth identification.

BMC oral health
BACKGROUND: Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be chal...

Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research.

BMC medical research methodology
OBJECTIVE: Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotate...

A dual-branch selective attention capsule network for classifying kiwifruit soft rot with hyperspectral images.

Scientific reports
Kiwifruit soft rot is highly contagious and causes serious economic loss. Therefore, early detection and elimination of soft rot are important for postharvest treatment and storage of kiwifruit. This study aims to accurately detect kiwifruit soft rot...

InsectSound1000 An insect sound dataset for deep learning based acoustic insect recognition.

Scientific data
InsectSound1000 is a dataset comprising more than 169000 labelled sound samples of 12 insects. The insect sound level spans from very loud (Bombus terrestris) to inaudible to human ears (Aphidoletes aphidimyza). The samples were extracted from more t...

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.

Scientific reports
The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir...