AIMC Topic: Deep Learning

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Parallel orthogonal deep neural network.

Neural networks : the official journal of the International Neural Network Society
Ensemble learning methods combine multiple models to improve performance by exploiting their diversity. The success of these approaches relies heavily on the dissimilarity of the base models forming the ensemble. This diversity can be achieved in man...

Deep learning-based detection and stage grading for optimising diagnosis of diabetic retinopathy.

Diabetes/metabolism research and reviews
AIMS: To establish an automated method for identifying referable diabetic retinopathy (DR), defined as moderate nonproliferative DR and above, using deep learning-based lesion detection and stage grading.

Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
BACKGROUND: Hepatic cystic echinococcosis is the main form of hepatic echinococcosis, which is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatment.

Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance ...

Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Cerebral microbleeds (CMBs) are cerebral small vascular diseases and are often used to diagnose symptoms such as stroke and dementia. Manual detection of cerebral microbleeds is a time-consuming and error-prone task, so the...

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better risk stratification, prognosis prediction, and therapy management. In this paper, we focused on the ...

Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning.

Magnetic resonance imaging
The accuracy of brain age estimates from magnetic resonance (MR) images has improved with the advent of deep learning artificial intelligence (AI) models. However, most previous studies on predicting age emphasized aging from childhood to adulthood a...

Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?

Sensors (Basel, Switzerland)
Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion...

NT-FDS-A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices.

Sensors (Basel, Switzerland)
Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities...

Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn's Disease, Based on Confocal Laser Endomicroscopy Images.

Journal of gastrointestinal and liver diseases : JGLD
BACKGROUND AND AIMS: Mucosal healing (MH) is associated with a stable course of Crohn's disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator's errors and automate assessment of CLE images, we used a deep...