AIMC Topic: Deep Learning

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Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms.

Frontiers in immunology
INTRODUCE: Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in s...

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

Frontiers in public health
INTRODUCTION: During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, an...

Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method.

Food chemistry
The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention netwo...

Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.

Annals of biomedical engineering
Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. ...

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions.

Journal of structural biology
Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a se...

Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
OBJECTIVE: Patients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learn...

Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.

Abdominal radiology (New York)
PURPOSE: To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magne...

Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Renal cell carcinoma represents a significant global health challenge with a low survival rate. The aim of this research was to devise a comprehensive deep-learning model capable of predicting survival probabilities in patie...

Deep Learning-Based Localization and Detection of Malpositioned Endotracheal Tube on Portable Supine Chest Radiographs in Intensive and Emergency Medicine: A Multicenter Retrospective Study.

Critical care medicine
OBJECTIVES: We aimed to develop a computer-aided detection (CAD) system to localize and detect the malposition of endotracheal tubes (ETTs) on portable supine chest radiographs (CXRs).

Deep learning for report generation on chest X-ray images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical imaging, specifically chest X-ray image analysis, is a crucial component of early disease detection and screening in healthcare. Deep learning techniques, such as convolutional neural networks (CNNs), have emerged as powerful tools for comput...