AIMC Topic: Deep Learning

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Developing tongue coating status assessment using image recognition with deep learning.

Journal of prosthodontic research
PURPOSE: To build an image recognition network to evaluate tongue coating status.

Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs...

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that pro...

An automatic progressive chromosome segmentation approach using deep learning with traditional image processing.

Medical & biological engineering & computing
The fully automatic chromosome analysis system plays an important role in the detection of genetic diseases, which in turn can reduce the diagnosis burden for cytogenetic experts. Chromosome segmentation is a critical step for such a system. However,...

Novel deep learning approach to estimate rigid gas permeable contact lens base curve for keratoconus fitting.

Contact lens & anterior eye : the journal of the British Contact Lens Association
INTRODUCTION: Rigid gas permeable contact lenses (RGP) are the most efficient means of providing optimal vision in keratoconus. RGP fitting can be challenging and time-consuming for ophthalmologists and patients. Deep learning predictive models could...

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) ba...

A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI.

Magnetic resonance imaging
Accurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis met...

Deep-Learning Terahertz Single-Cell Metabolic Viability Study.

ACS nano
Cell viability assessment is critical, yet existing assessments are not accurate enough. We report a cell viability evaluation method based on the metabolic ability of a single cell. Without culture medium, we measured the absorption of cells to tera...

Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction.

European journal of radiology
PURPOSE: To assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learni...

Deep learning-driven adaptive optics for single-molecule localization microscopy.

Nature methods
The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorle...