AIMC Topic: Deep Learning

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An effective segmentation and attention-based reptile residual capsule auto encoder for pest classification.

Pest management science
PURPOSE: Insect pests are a major global factor affecting agricultural crop productivity and quality. Rapid and precise insect pest detection is crucial for improving handling and prediction techniques. There are several methods for pest detection an...

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.

Medical image analysis
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with s...

A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or the...

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Bone
Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bla...

Diabetic retinopathy prediction based on vision transformer and modified capsule network.

Computers in biology and medicine
Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it increases as long as a person suffers from diabetes. Protecting the sight of the patient or decelerating...

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning.

Environmental pollution (Barking, Essex : 1987)
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there r...

Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.

Computers in biology and medicine
This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentatio...

Deep learning for low-data drug discovery: Hurdles and opportunities.

Current opinion in structural biology
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such ...

Persistent Luminescence Lifetime-Based Near-Infrared Nanoplatform via Deep Learning for High-Fidelity Biosensing of Hypochlorite.

Analytical chemistry
In light of deep tissue penetration and ultralow background, near-infrared (NIR) persistent luminescence (PersL) bioprobes have become powerful tools for bioapplications. However, the inhomogeneous signal attenuation may significantly limit its appli...

Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

Journal of applied clinical medical physics
PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-...