AIMC Topic: Deep Learning

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Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.

European radiology experimental
BACKGROUND: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores ...

Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.

Communications biology
In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit pro...

Optimizing dental implant identification using deep learning leveraging artificial data.

Scientific reports
This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using...

A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends.

Biomedical physics & engineering express
Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ult...

Automatic segmentation of the midfacial bone surface from ultrasound images using deep learning methods.

International journal of oral and maxillofacial surgery
With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D bone modelling has attracted much attention, and its accuracy has been studied for the femur, tibia...

Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan.

Investigative radiology
OBJECTIVE: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based ite...

Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.

Clinical nuclear medicine
PURPOSE: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Mor...

Deep Learning and Hyperspectral Imaging for Liver Cancer Staging and Cirrhosis Differentiation.

Journal of biophotonics
Liver malignancies, particularly hepatocellular carcinoma (HCC), pose a formidable global health challenge. Conventional diagnostic techniques frequently fall short in precision, especially at advanced HCC stages. In response, we have developed a nov...

Caps-ac4C: An effective computational framework for identifying N4-acetylcytidine sites in human mRNA based on deep learning.

Journal of molecular biology
N4-acetylcytidine (ac4C) is a crucial post-transcriptional modification in human mRNA, involving the acetylation of the nitrogen atom at the fourth position of cytidine. This modification, catalyzed by N-acetyltransferases such as NAT10, is primarily...

Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis.

Journal of X-ray science and technology
ObjectiveThe goal of this study is to assess the effectiveness of a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms to detect lung cancer early from CT scan images. The study aims to improve diagnostic accuracy, se...