AIMC Topic: Tomography, X-Ray Computed

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Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Interdisciplinary sciences, computational life sciences
Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biops...

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

European radiology
OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with r...

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

Scientific reports
In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise li...

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.

Medical image analysis
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandem...

Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging.

Clinical lung cancer
BACKGROUND: We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patient...

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

Medical image analysis
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, ches...

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

Medical physics
PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that ar...

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

European journal of radiology
PURPOSE: As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their ris...