LEAPSE: Learning Environment Affordances for 3D Human Pose and Shape Estimation.

We live in a 3D world where people interact with each other in the environment. Learning 3D posed humans therefore requires us to perceive and interpret these interactions. This paper proposes LEAPSE, a novel method that learns salient instance affordances for estimating a posed body from a single RGB image in a non-parametric manner.

Robust Ship Detection in Infrared Images through Multiscale Feature Extraction and Lightweight CNN.

The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of multi-scene, multi-resolution and multi-type target ships. To overcome these challenges, a ship detection method based on multiscale feature extraction and lightweight CNN is proposed. Firstly, the candidate-region extraction method, based on a multiscale model, can cover the potential targets under different backgrounds accurately.

Segmentation of coronary arteries images using global feature embedded network with active contour loss.

Coronary heart disease (CHD) is a serious disease that endangers human health and life. In recent years, the morbidity and mortality of CHD are increasing significantly. Because of the particularity and complexity of medical image, it is challenging to segment coronary artery accurately and efficiently.

Distributed Optimization of Multiagent Systems Subject to Inequality Constraints.

In this paper, we study a distributed convex optimization problem with inequality constraints. Each agent is associated with its cost function, and can only exchange information with its neighbors. It is assumed that each cost function is convex and the optimization variable is subject to an inequality constraint.

Value of Diffusion-Weighted Magnetic Resonance Imaging Combined with miR-18a Level in Predicting Radiosensitivity of Cervical Cancer.

BACKGROUND Radioresistance during radiotherapy of cervical cancer often leads to treatment failure; therefore, there is an urgent need to develop effective predictive indicators of radiosensitivity for cervical cancer patients. MATERIAL AND METHODS Cervical cancer cells were collected from 40 patients who received surgical resections. The relationships between apparent diffusion coefficient (ADC) values of masses before surgery and different micro-RNAs (miRNA) levels (miR-18a, miR-132, and miR-145) of these cells were investigated.

Diagnostic significance of magnetic resonance imaging in patients with cervical cancer after brachytherapy: a meta-analysis.

Background: Patients with uterine cervical cancer suffer high mortality. Accurate detection of a residual tumor by magnetic resonance imaging (MRI) during and after directed brachytherapy (BCT) is crucial for the success of cancer treatment and is a significant predictor of patient survival.

Purpose: To determine the diagnostic significance of MRI in detecting residual tumor tissue after BCT.

Correlation of diffusion tensor imaging parameters and Gleason scores of prostate cancer.

The aim of the present study was to explore the association between the parameters of diffusion tensor imaging (DTI), including fractional anisotropy (FA) values, apparent diffusion coefficient (ADC) values and the diffusion tensor tractography (DTT) map, with the Gleason score of prostate cancer (PCa). A retrospective study of 50 cases of PCa confirmed by biopsy or surgical pathology was performed. Conventional magnetic resonance imaging and DTI scans were conducted in these cases.