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Smoking & Tobacco

Latest AI and machine learning research in smoking & tobacco for healthcare professionals.

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Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function.

With the widespread use of biometric authentication comes the exploitation of presentation attacks, ...

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

BACKGROUND: Twitter presents a valuable and relevant social media platform to study the prevalence o...

Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning.

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the d...

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.

Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying ...

Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (...

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor ...

Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI.

In this paper, we present an evaluation of four encoder-decoder CNNs in the segmentation of the pros...

Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

BACKGROUND: Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopn...

Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.

Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tis...

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.

Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background p...

Cellular community detection for tissue phenotyping in colorectal cancer histology images.

Classification of various types of tissue in cancer histology images based on the cellular compositi...

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network.

Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segment...

Robot-Assisted Radical Prostatectomy Associated with Decreased Persistent Postoperative Opioid Use.

Minimally invasive surgery offers reduced pain and opioid use postoperatively compared with open su...

Automatic 3D landmarking model using patch-based deep neural networks for CT image of oral and maxillofacial surgery.

BACKGROUND: Manual landmarking is a time consuming and highly professional work. Although some algor...

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (EC...

Introducing Hann windows for reducing edge-effects in patch-based image segmentation.

There is a limitation in the size of an image that can be processed using computationally demanding ...

CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway.

Studies on the influence of a modern lifestyle in abetting Coronary Heart Diseases (CHD) have mostly...

Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.

Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap...

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