AIMC Topic: Tomography, X-Ray Computed

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A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

The European respiratory journal
Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation ...

Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose-Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X-Ray, and Point-of-Care Ultrasound Images for Quality.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Deep learning for medical imaging analysis uses convolutional neural networks pretrained on ImageNet (Stanford Vision Lab, Stanford, CA). Little is known about how such color- and scene-rich standard training images compare quantitatively...

Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Chest computed tomography (CT) is crucial for the detection of lung cancer, and many automated CT evaluation methods have been proposed. Due to the divergent software dependencies of the reported approaches, the developed methods are rare...

Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.

Physics in medicine and biology
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, m...

Data mining for sex estimation based on cranial measurements.

Forensic science international
The aim of the present study is to develop effective and understandable classification models for sex estimation and to identify the most dimorphic linear measurements in adult crania by means of data mining techniques. Furthermore, machine learning ...

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.

Journal of infection and public health
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes. Cancerous cells are abnormal areas often growing in any part of human body that are life-threatening. Cancer also known as tumor must be qui...

Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a chal...

A Simple Method to Train the AI Diagnosis Model of Pulmonary Nodules.

Computational and mathematical methods in medicine
BACKGROUND: The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonar...

A robust convolutional neural network for lung nodule detection in the presence of foreign bodies.

Scientific reports
Lung cancer is a major cause of death worldwide. As early detection can improve outcome, regular screening is of great interest, especially for certain risk groups. Besides low-dose computed tomography, chest X-ray is a potential option for screening...