AIMC Topic: Deep Learning

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Identification of Barrett's esophagus in endoscopic images using deep learning.

BMC gastroenterology
BACKGROUND: Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.

Deep learning with language models improves named entity recognition for PharmaCoNER.

BMC bioinformatics
BACKGROUND: The recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Although considerable efforts have...

Improving deep learning method for biomedical named entity recognition by using entity definition information.

BMC bioinformatics
BACKGROUND: Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER te...

Deep Learning-Based Cervical Spine Posterior Percutaneous Endoscopic Disc Nucleus Resection for the Treatment of Cervical Spondylotic Radiculopathy.

Journal of healthcare engineering
In the past 10 years, the technology of percutaneous spine endoscopy has been continuously developed. The indications have expanded from simple lumbar disc herniation to various degenerative diseases of the cervical, thoracic, and lumbar spine. Tradi...

Application of CT Medical Imaging Combined with Deep Learning 3D Reconstruction in the Diagnosis and Rehabilitation of Anterior Cruciate Ligament Injury in Table Tennis Players.

Journal of healthcare engineering
Because of the intense competition, table tennis requires players to bear a strong physiological load, which increases the risk of sports injury. Anterior cruciate ligament (ACL) is an important structure of the knee joint to maintain forward stabili...

Detecting the content of the bright blue pigment in cream based on deep learning and near-infrared spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The excessive content of additives in food is a radical problem that affects human health. However, traditional chemical methods are limited by a long cycle, low accuracy, and strong destructiveness, so a fast and accurate alternative is urgently nee...

Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To show that a deep learning (DL)-based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the "grou...

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

Computers in biology and medicine
BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order ...

Deep Learning for Radiotherapy Outcome Prediction Using Dose Data - A Review.

Clinical oncology (Royal College of Radiologists (Great Britain))
Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy...

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies.

Abdominal radiology (New York)
PURPOSE: In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiati...