AIMC Topic: Deep Learning

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Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

BMC cancer
PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed...

Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.

Scientific reports
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development o...

DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning.

Nature communications
To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their tech...

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthe...

Deep learning methods for fully automated dental age estimation on orthopantomograms.

Clinical oral investigations
OBJECTIVES: This study aimed to use all permanent teeth as the target and establish an automated dental age estimation method across all developmental stages of permanent teeth, accomplishing all the essential steps of tooth determination, tooth deve...

High-resolution density assessment assisted by deep learning of (Lamarck, 1816) and (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay.

PeerJ
This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 "Reefs" habitat, and , in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline ...

Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data.

PloS one
BACKGROUND: Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may le...

Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma.

Asian journal of surgery
PURPOSE: It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs) in papillary thyroid carcinoma (PTC) patients. This study aims to develop deep-learning models for the automatic segmentation and classification of L...

Comparing code-free and bespoke deep learning approaches in ophthalmology.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
AIM: Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writing code. In this review, we comprehensively review the advantages that CFDL offers over bespoke expe...

Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm.

Journal of imaging informatics in medicine
This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the tracheal diameter and lung volume. In this retrospective study, a total of 221 patients (71.1 ± 12.4 years, 174 males) who underwent vascular dynamic...