AIMC Topic: Deep Learning

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Saliency-driven system models for cell analysis with deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Saliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert's visual attention, there is little ...

Caries Detection with Near-Infrared Transillumination Using Deep Learning.

Journal of dental research
Dental caries is the most prevalent chronic condition worldwide. Early detection can significantly improve treatment outcomes and reduce the need for invasive procedures. Recently, near-infrared transillumination (TI) imaging has been shown to be eff...

Caveolae and scaffold detection from single molecule localization microscopy data using deep learning.

PloS one
Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. ...

A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI.

IEEE transactions on medical imaging
The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid diagnosis, treatment and tracking the progression of different neurologic diseases. Medical image data are volumetric and some neural network models for medical...

Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms.

Journal of neurointerventional surgery
BACKGROUND: Angiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a l...

Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.

Endoscopy
BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly a...

Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology.

ChemMedChem
Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and r...

Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network.

The British journal of dermatology
BACKGROUND: Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carc...

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

IEEE transactions on visualization and computer graphics
This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D m...