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Feature-targeted deep learning framework for pulmonary tumorous Cone-beam CT (CBCT) enhancement with multi-task customized perceptual loss and feature-guided CycleGAN.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Thoracic Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for lung cancer treatments. However, CBCT images often suffer from streaking artifacts an...

Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound.

The American journal of emergency medicine
RATIONALE: Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, curre...

Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

PloS one
In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, tr...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Scientific reports
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early d...

Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning.

Computers in biology and medicine
Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance segmentation methods rely heavily on fully annotated, large-scale training datasets. However, manua...

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Scientific reports
Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalitie...

[Application of artificial intelligence in combination with CT radiomics in chronic obstructive pulmonary disease].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
As CT imaging is increasingly used for the evaluation of lung nodules and the diagnosis and screening of lung cancer in smokers, we have more opportunities to use CT images to identify patients with early-stage chronic obstructive pulmonary disease(C...

Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.

Radiology. Artificial intelligence
Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated...