STFormer: Spatial-Temporal-Aware Transformer for Video Instance Segmentation.

Video instance segmentation (VIS) is a challenging task, requiring handling object classification, segmentation, and tracking in videos. Existing Transformer-based VIS approaches have shown remarkable success, combining encoded features and instance queries as decoder inputs. However, their decoder inputs are low-resolution due to computational cost, resulting in a loss of fine-grained information, sensitivity to background interference, and poor handling of small objects.

Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach.

Objectives: To investigate the value of CT radiomics combined with radiological features in predicting pathological grade of stage I invasive pulmonary adenocarcinoma (IPA) based on the International Association for the Study of Lung Cancer (IASLC) new grading system.

Methods: The preoperative CT images and clinical information of 294 patients with stage I IPA were retrospectively analyzed (159 training set; 69 validation set; 66 test set). Referring to the IASLC new grading system, patients were divided into a low/intermediate-grade group and a high-grade group.

A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning.

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison.

A Closer Look at the Joint Training of Object Detection and Re-identification in Multi-Object Tracking.

Unifying object detection and re-identification (ReID) into a single network enables faster multi-object tracking (MOT), while this multi-task setting poses challenges for training. In this work, we dissect the joint training of detection and ReID from two dimensions: label assignment and loss function. We find previous works generally overlook them and directly borrow the practices from object detection, inevitably causing inferior performance.

The diagnostic value of dynamic volume computed tomography angiography in children with anomalous origin of the left coronary artery from the pulmonary artery.

There have been almost no reports on the technique of dynamic volume computed tomography angiography (DVCTA) in children with anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA). Twelve children with ALCAPA, aged 5 months to 15 years, were enrolled in this retrospective study to explore the clinical value of DVCTA in the diagnosis of ALCAPA in children. All patients underwent low-dose prospective ECG-gated 320-slice DVCTA and transthoracic echocardiography.

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.

Nocal-Siam: Refining Visual Features and Response With Advanced Non-Local Blocks for Real-Time Siamese Tracking.

Siamese trackers contain two core stages, i.e., learning the features of both target and search inputs at first and then calculating response maps via the cross-correlation operation, which can also be used for regression and classification to construct typical one-shot detection tracking framework.

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis.

Hypertension Control in Adults With CKD in China: Baseline Results From the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE).

Background: Hypertension contributes to increased morbidity and mortality in the chronic kidney disease (CKD) population. Studies on blood pressure control in CKD patients in China are limited. In this study, we aimed to describe the status of blood pressure control in Chinese CKD patients based on the first national prospective CKD cohort data.