AIMC Topic: Deep Learning

Clear Filters Showing 23911 to 23920 of 28423 articles

Deep Learning Models for CT Segmentation of Invasive Pulmonary Aspergillosis, Mucormycosis, Bacterial Pneumonia and Tuberculosis: A Multicentre Study.

Mycoses
BACKGROUND: The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesio...

[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery
To development a deep learning(DL) model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma(NPC) and nasopharyngeal lymphoma(NPL). The retrospective study included 142 patients with NPL and 292...

Slice-Inference-Assisted Lightweight Small Object Detection Model for Holographic Digital Immunoassay Quantification.

Analytical chemistry
Sensitive and cost-effective detection methods utilizing portable equipment are crucial for applications in food safety inspection, environmental monitoring, and clinical diagnosis. In this study, we propose a sliced inference-assisted lightweight sm...

Deep Learning-Assisted Nanocavity Sensor for Amphiphilic Biomarker Analysis.

Analytical chemistry
Fluorescence enhancement using nanocavity structures is a promising approach for rapid detection of amphiphilic biomarkers, which are essential for diagnosing diseases such as cancer and infections. This paper presents the use of a silver nanocube (A...

ConsAMPHemo: A computational framework for predicting hemolysis of antimicrobial peptides based on machine learning approaches.

Protein science : a publication of the Protein Society
Many antimicrobial peptides (AMPs) function by disrupting the cell membranes of microbes. While this ability is crucial for their efficacy, it also raises questions about their safety. Specifically, the membrane-disrupting ability could lead to hemol...

A Deep Learning Model Based on High-Frequency Ultrasound Images for Classification of Different Stages of Liver Fibrosis.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: To develop a deep learning model based on high-frequency ultrasound images to classify different stages of liver fibrosis in chronic hepatitis B patients.

Novel fusion-based time-frequency analysis for early prediction of sudden cardiac death from electrocardiogram signals.

Medical engineering & physics
Sudden cardiac death (SCD) is one of the leading causes of global mortality, often occurring without warning and driven by complex cardiac dynamics. Despite significant advances in cardiovascular diagnostics, accurately predicting SCD at an early sta...

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Radiology. Imaging cancer
Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communications in Medicine-based deep learning-based denoising (DLD), in evaluating small renal masses (SRM...

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

The journal of pathology. Clinical research
The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, identifying the spatial organization of diverse cell types that significantly correlates with patient pr...