AIMC Topic: Deep Learning

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DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Methods (San Diego, Calif.)
Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protei...

SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals.

Computers in biology and medicine
Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional machine learning models. However, unlike other fields, EEG decoding lacks comprehensive open-source libraries dedicated to neural networks. Existing tools...

Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models.

Computers in biology and medicine
Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of dee...

Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and e...

Scaling data toward pan-cancer foundation models.

Trends in cancer
Recent advances in artificial intelligence (AI) have revolutionized computational pathology (CPath), particularly through deep learning (DL) and neural networks (NNs). In a recent study, Vorontsov et al. introduced Virchow, a new foundation model (FM...

Multi-receptor skin with highly sensitive tele-perception somatosensory.

Science advances
The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognit...

A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar.

Sensors (Basel, Switzerland)
Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a p...

Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.

International journal of molecular sciences
Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the de...

Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.

European journal of medical research
BACKGROUND: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in ...

Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.

BMC medical imaging
This systematic review aimed to evaluate the potential of deep learning algorithms for converting low-dose Positron Emission Tomography (PET) images to full-dose PET images in different body regions. A total of 55 articles published between 2017 and ...