AIMC Topic: Neural Networks, Computer

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Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in an advanced stage. Although the recent development of narrow-band imaging (NBI) and increased awareness among endoscopists have enabled detection of superf...

Towards fully automated third molar development staging in panoramic radiographs.

International journal of legal medicine
Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the ...

A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue co...

Artificial intelligence and machine learning in nephropathology.

Kidney international
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biops...

Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object localization, semantic segmentation, and object instance segmentation. Accordingly, the applicabili...

Addressing class imbalance in deep learning for small lesion detection on medical images.

Computers in biology and medicine
Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This ...

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides.

JAMA network open
IMPORTANCE: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopatho...

A convolution neural network for higher resolution dose prediction in prostate volumetric modulated arc therapy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose.

Multi-Level-Phase Deep Learning Using Divide-and-Conquer for Scaffolding Safety.

International journal of environmental research and public health
A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety predi...