AIMC Topic: Deep Learning

Clear Filters Showing 10571 to 10580 of 28423 articles

A two-step deep learning method for 3DCT-2DUS kidney registration during breathing.

Scientific reports
This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which comprises a feature network, and a 3D-2D CNN-based registration network. The feature network has handcrafted texture featur...

Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms.

Lab on a chip
Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion bl...

Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023.

Frontiers in public health
INTRODUCTION: Physical fitness is regarded as a significant indicator of sarcopenia. This study aimed to develop and evaluate a deep-learning model for predicting the decline in physical fitness due to sarcopenia in individuals with potential sarcope...

An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
BACKGROUND: In this article, we present a computerized system for the analysis and assessment of diabetic retinopathy (DR) based on retinal fundus photographs. DR is a chronic ophthalmic disease and a major reason for blindness in people with diabete...

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation.

Physical and engineering sciences in medicine
In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a metal artifa...

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Computational biology and chemistry
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-c...

Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios.

Physical and engineering sciences in medicine
This study aimed to investigate the robustness of a deep learning (DL) fusion model for low training-to-test ratio (TTR) datasets in the segmentation of gross tumor volumes (GTVs) in three-dimensional planning computed tomography (CT) images for lung...

Tumor detection under cystoscopy with transformer-augmented deep learning algorithm.

Physics in medicine and biology
Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive ...

Deep learning for the prediction of the chemotherapy response of metastatic colorectal cancer: comparing and combining H&E staining histopathology and infrared spectral histopathology.

The Analyst
Colorectal cancer is a global public health problem with one of the highest death rates. It is the second most deadly type of cancer and the third most frequently diagnosed in the world. The present study focused on metastatic colorectal cancer (mCRC...

FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA.

IEEE journal of biomedical and health informatics
Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learni...