“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images

A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization ability. This study presents Cimatec-CovNet-19, a new light 3D convolutional neural network inspired by the VGG16 architecture that supports COVID-19 identification from chest CT scans. We trained the algorithm with a dataset of 3000 CT Scans (1500 COVID-19-positive) with images from different parts of the world, enhanced with 3000 images obtained with data augmentation techniques. We introduced a novel pre-processing approach to perform a slice-wise selection based solely on the lung CT masks and an empirically chosen threshold for the very first slice. It required only 16 slices from a CT examination to identify COVID-19. The model achieved a recall of 0.88, specificity of 0.88, ROC-AUC of 0.95, PR-AUC of 0.95, and F1-score of 0.88 on a test set with 414 samples (207 COVID-19). These results support Cimatec-CovNet-19 as a good and light screening tool for COVID-19 patients. The whole code is freely available for the scientific community.

/pdf/a-light-deep-learning-algorithm-for-ct-diagnosis-of-covid-19-1vp5qxfr.pdf

A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia

Detection of SARS-CoV-2 Virus Using Lightweight Convolutional Neural Networks

Chest cancer is a common and potentially life-threatening disease that affects humanity worldwide. Early detection of chest cancer is critical for successful treatment and improved survival rates. Medical technology advancements in recent years have resulted in the development of various methods for detecting chest cancer. These methods have significantly improved the accuracy and reliability of chest cancer detection. However, the challenge remains to identify chest cancer at an early stage when it is most treatable. Therefore, ongoing research is focused on developing new and innovative approaches to enhance the resolution of CT chest images for early detection of chest cancer. In this paper the Self- Governing Assessment Network (SGAN) model is proposed to produce super resolution images from the low-resolution CT chest images. The proposed SGAN model is tested on 900 chest CT images dataset for super resolution factors 2, 4 and 6 respectively. The proposed model outperformed the existing models with PSNR values 35.82, 36.732, 37.196 and SSIM values 0.914, 0.9421, 0.9653 for super resolution factors 2,4 and 6 respectively. 

Self-Governing Assessment Network (SGAN) Based Super-Resolution for CT chest images

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image analysis. The lungs, bone, and mediastinum were included in terms of anatomy, while X-ray and computed tomography (CT), with and without contrast enhancement, were considered regarding imaging modalities. Four key components of deep learning were summarized, namely, network architectures, learning strategies, optimization methods, and vision tasks. Disease-specific applications were discussed in detail with respect to the dimension of the data input, network architecture, and modality: lung cancer, pneumonia, tuberculosis, pulmonary embolism, chronic obstructive pulmonary disease, and interstitial lung disease for lung; traumatic fractures, osteoporosis, osteoporotic fractures, and bone metastases for bone; and coronary artery calcification and aortic dissection for vascular diseases. Finally, five promising research directions and possible solutions were presented for future work.

Recent developments in the application of artificial intelligence to chest medical image analysis

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Recent developments in the application of artificial intelligence to chest medical image analysis