AIMC Topic: Deep Learning

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A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety.

Sensors (Basel, Switzerland)
The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations...

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning.

Physics in medicine and biology
To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.. A clinical data set of 58 pre- and post-radiotherapyTc-...

Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Deep-learning algorithms (DLAs) have been used in artificial intelligence aided ultrasonography diagnosis of thyroid and breast lesions. However, its use has not been described in the case of dermatologic ultrasound lesions. Our purpose w...

SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer.

International journal of molecular sciences
Identifying secretory proteins from blood, saliva or other body fluids has become an effective method of diagnosing diseases. Existing secretory protein prediction methods are mainly based on conventional machine learning algorithms and are highly de...

Efficacy of a deep leaning model created with the transfer learning method in detecting sialoliths of the submandibular gland on panoramic radiography.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: This study aimed to compare the performance of 3 deep learning models, including a model constructed with the transfer learning method, in detecting submandibular gland sialoliths on panoramic radiographs.

Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning.

European radiology
OBJECTIVES: The molecular subtyping of diffuse gliomas is important. The aim of this study was to establish predictive models based on preoperative multiparametric MRI.

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

Magnetic resonance imaging
PURPOSE: To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) ...

Semi-HIC: A novel semi-supervised deep learning method for histopathological image classification.

Computers in biology and medicine
Histopathological images provide a gold standard for cancer recognition and diagnosis. Existing approaches for histopathological image classification are supervised learning methods that demand a large amount of labeled data to obtain satisfying perf...

Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.

Acta neuropathologica communications
Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting semiquantitative scores often vary between individual raters and research centers, limiting statistical...

AttentionDDI: Siamese attention-based deep learning method for drug-drug interaction predictions.

BMC bioinformatics
BACKGROUND: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pair...