AIMC Topic: Tomography, X-Ray Computed

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End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

JCO clinical cancer informatics
PURPOSE: Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatme...

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Me...

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) tech...

[Clinical significance of the deep learning algorithm based on contrast-enhanced CT in the differential diagnosis of gastric gastrointestinal stromal tumors with a diameter ≤ 5 cm].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery
Contrast-enhanced CT is an important method of preoperative diagnosis and evaluation for the malignant potential of gastric submucosal tumor (SMT). It has a high diagnostic accuracy rate in differentiating gastric gastrointestinal stromal tumor (GIS...

Application of machine learning in CT images and X-rays of COVID-19 pneumonia.

Medicine
Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-r...

A deep learning based automatic segmentation approach for anatomical structures in intensity modulation radiotherapy.

Mathematical biosciences and engineering : MBE
OBJECTIVE: To evaluate the automatic segmentation approach for organ at risk (OARs) and compare the parameters of dose volume histogram (DVH) in radiotherapy.

[Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To explore the clinical feasibility of applying deep learning (DL) reconstruction algorithm in low-dose thin-slice liver CT examination of healthy volunteers by comparing the reconstruction algorithm based on DL, filtered back projection (...

[Automatic segmentation of kidney tumor based on cascaded multiscale convolutional neural networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The background of abdominal computed tomography (CT) images is complex, and kidney tumors have different shapes, sizes and unclear edges. Consequently, the segmentation methods applying to the whole CT images are often unable to effectively segment t...

Auto informing COVID-19 detection result from x-ray/CT images based on deep learning.

The Review of scientific instruments
It is no secret to all that the corona pandemic has caused a decline in all aspects of the world. Therefore, offering an accurate automatic diagnostic system is very important. This paper proposed an accurate COVID-19 system by testing various deep l...