AIMC Topic: Deep Learning

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Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.

BMC oral health
BACKGROUND: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence.

Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images.

Scientific reports
The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, ...

Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop.

Scientific reports
The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechan...

Classification of land lot shapes in real estate sector using a convolutional neural network.

PloS one
In the agriculture and real estate industries, land lot shapes have mostly been classified by visual inspection or hard-crafted rules. These conventional methods are time-consuming, resource-intensive, and subject to human bias. This study aims to fi...

Deep learning-enabled fluorescence imaging for surgical guidance: training for oral cancer depth quantification.

Journal of biomedical optics
SIGNIFICANCE: Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth p...

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

Academic radiology
RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT). However, the associated ionizing radiation poses long-term risks. Modern artificial intelligence re...

Automated diagnosis of atherosclerosis using multi-layer ensemble models and bio-inspired optimization in intravascular ultrasound imaging.

Medical & biological engineering & computing
Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physica...

Two-step deep-learning identification of heel keypoints from video-recorded gait.

Medical & biological engineering & computing
Accurate and fast extraction of step parameters from video recordings of gait allows for richer information to be obtained from clinical tests such as Timed Up and Go. Current deep-learning methods are promising, but lack in accuracy for many clinica...

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

Annals of biomedical engineering
Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, wh...