AIMC Topic: Deep Learning

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Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

BMC medical imaging
BACKGROUND: Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. Th...

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

Journal of medical systems
There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screeni...

Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images.

Alcoholism, clinical and experimental research
BACKGROUND: Seeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision-making, and hazardous drinking. Due to the exponential growth of (social) media use it is important to develop algorithms to quantify alcohol ex...

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

Computational and mathematical methods in medicine
Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in co...

Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS (NECRS) on preoperative CT.

A review on deep-learning algorithms for fetal ultrasound-image analysis.

Medical image analysis
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. A number of survey papers in the field is today available, but most of them are focusing on a broader area of medical-image analysis or not covering ...

Segmentation of Vestibular Schwannomas on Postoperative Gadolinium-Enhanced T1-Weighted and Noncontrast T2-Weighted Magnetic Resonance Imaging Using Deep Learning.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVE: Surveillance of postoperative vestibular schwannomas currently relies on manual segmentation and measurement of the tumor by content experts, which is both labor intensive and time consuming. We aimed to develop and validate deep learning ...

A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model.

Endocrine pathology
Core needle biopsy (CNB) is a method that can be used as an alternative to fine-needle aspiration for the sampling of thyroid nodules to provide a preoperative diagnosis. Nuclear atypia is of paramount importance in diagnosing thyroid tumors. We aime...

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.

Computers in biology and medicine
According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and...

Cx22: A new publicly available dataset for deep learning-based segmentation of cervical cytology images.

Computers in biology and medicine
The segmentation of cervical cytology images plays an important role in the automatic analysis of cervical cytology screening. Although deep learning-based segmentation methods are well-developed in other image segmentation areas, their application i...