AIMC Topic: Deep Learning

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Contrastive learning in brain imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Contrastive learning is a type of deep learning technique trying to classify data or examples without requiring data labeling. Instead, it learns about the most representative features that contrast positive and negative pairs of examples. In literat...

Regional Image Quality Scoring for 2-D Echocardiography Using Deep Learning.

Ultrasound in medicine & biology
OBJECTIVE: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.

A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario.

Computers in biology and medicine
BACKGROUND: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial for effective treatment, yet conv...

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Analytical chemistry
Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database sear...

Artificial Intelligence in Pancreatic Imaging: A Systematic Review.

United European gastroenterology journal
The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to...

Stacked long and short-term memory (SLSTM) - assisted terahertz spectroscopy combined with permutation importance for rapid red wine varietal identification.

Talanta
Mislabeling of low-value red wines as high-value ones is common, which seriously undermines consumer rights and interests. However, traditional sensory and chemical analysis methods have limitations, which highlights the need for novel detection tech...

Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.

European radiology
OBJECTIVES: Forensic age estimation from orthopantomograms (OPGs) can be performed more quickly and accurately using convolutional neural networks (CNNs), making them an ideal extension to standard forensic age estimation methods. This study evaluate...

Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data.

Medical & biological engineering & computing
Photoacoustic tomography (PAT) has emerged as a promising imaging modality for breast cancer detection, offering unique advantages in visualizing tissue composition without ionizing radiation. However, limited-view scenarios in clinical settings pres...

A deep learning pipeline for systematic and accurate vertebral fracture reporting in computed tomography.

Clinical radiology
AIM: Spine fractures are a frequent and relevant diagnosis, but systematic documentation is time-consuming and sometimes overlooked. A deep learning pipeline for opportunistic fracture detection in computed tomography (CT) spine images of varying fie...

A bio-lattice deep learning framework for modeling discrete biological materials.

Journal of the mechanical behavior of biomedical materials
Biological tissues dynamically adapt their mechanical properties at the microscale in response to stimuli, often governed by discrete interacting mechanisms that dictate the material's behavior at the macroscopic scale. An approach to model the discr...