AIMC Topic: Diagnostic Imaging

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Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging.

Dento maxillo facial radiology
Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial ...

Data augmentation for medical imaging: A systematic literature review.

Computers in biology and medicine
Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it pos...

Artificial intelligence in veterinary diagnostic imaging: A literature review.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imag...

Deep Learning Based Infrared Thermal Image Analysis of Complex Pavement Defect Conditions Considering Seasonal Effect.

Sensors (Basel, Switzerland)
Deep learning techniques underpinned by extensive data sources encompassing complex pavement features have proven effective in early pavement damage detection. With pavement features exhibiting temperature variation, inexpensive infra-red imaging tec...

Unsupervised landmark detection and classification of lung infection using transporter neural networks.

Computers in biology and medicine
Supervised deep learning techniques have been very popular in medical imaging for various tasks of classification, segmentation, and object detection. However, they require a large number of labelled data which is expensive and requires many hours of...

A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.

Nature neuroscience
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the ...

Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data.

Computational intelligence and neuroscience
The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant prot...

An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: a Methodological Approach.

Journal of digital imaging
Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either ha...

RGBD Salient Object Detection, Based on Specific Object Imaging.

Sensors (Basel, Switzerland)
RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can m...

Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis.

Sensors (Basel, Switzerland)
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that...