AIMC Topic: Deep Learning

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Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images....

Lung cancer prediction by Deep Learning to identify benign lung nodules.

Lung cancer (Amsterdam, Netherlands)
INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an in...

Calibration of the EBT3 Gafchromic Film Using HNN Deep Learning.

BioMed research international
To achieve a dose distribution conformal to the target volume while sparing normal tissues, intensity modulation with steep dose gradient is used for treatment planning. To successfully deliver such treatment, high spatial and dosimetric accuracy are...

Deep Learning for EEG Seizure Detection in Preterm Infants.

International journal of neural systems
EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are re...

Deep Learning for Imaging and Detection of Microorganisms.

Trends in microbiology
Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic...

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.

American journal of ophthalmology
PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images...

A survey on incorporating domain knowledge into deep learning for medical image analysis.

Medical image analysis
Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external informat...

Deep learning for the radiographic diagnosis of proximal femur fractures: Limitations and programming issues.

Orthopaedics & traumatology, surgery & research : OTSR
INTRODUCTION: Radiology is one of the domains where artificial intelligence (AI) yields encouraging results, with diagnostic accuracy that approaches that of experienced radiologists and physicians. Diagnostic errors in traumatology are rare but can ...

Automatic detection of brain metastases on contrast-enhanced CT with deep-learning feature-fused single-shot detectors.

European journal of radiology
PURPOSE: Despite the potential usefulness, no automatic detector is available for brain metastases on contrast-enhanced CT (CECT). The study aims to develop and investigate deep learning-based detectors for brain metastases detection on CECT.

Segmentation Approaches for Diabetic Foot Disorders.

Sensors (Basel, Switzerland)
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabe...