MC-NeRF: Multi-Camera Neural Radiance Fields for Multi-Camera Image Acquisition Systems.

Neural Radiance Fields (NeRF) use multi-view images for 3D scene representation, demonstrating remarkable performance. As one of the primary sources of multi-view images, multi-camera systems encounter challenges such as varying intrinsic parameters and frequent pose changes. Most previous NeRF-based methods assume a unique camera and rarely consider multi-camera scenarios.

Fear of cancer recurrence and coping strategies among patients with oral cancer: the impact on post-traumatic growth.

Objective: Assess the effect of fear of cancer recurrence (FCR) and coping strategies on patients with oral cancer's post-traumatic growth (PTG).

Methods: A total of 255 patients with oral cancer participated and were investigated using the Chinese version of the Posttraumatic Growth Inventory (PTGI), the Fear of Progression Questionnaire-Short Form (FOP-Q-SF), and the Simplified Coping Style Questionnaire (SCSQ) in this cross-sectional study. Descriptive, univariate, Pearson correlation analyses, and multiple linear regression analyses were performed.

Full Transformer Framework for Robust Point Cloud Registration With Deep Information Interaction.

Point cloud registration is an essential technology in computer vision and robotics. Recently, transformer-based methods have achieved advanced performance in point cloud registration by utilizing the advantages of the transformer in order-invariance and modeling dependencies to aggregate information. However, they still suffer from indistinct feature extraction, sensitivity to noise, and outliers, owing to three major limitations: 1) the adoption of CNNs fails to model global relations due to their local receptive fields, resulting in extracted features susceptible to noise; 2) the shallow-wide architecture of transformers and the lack of positional information lead to indistinct feature extraction due to inefficient information interaction; and 3) the insufficient consideration of geometrical compatibility leads to the ambiguous identification of incorrect correspondences.

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer.

Objective: To investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).

Methods: This was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016.

Prediction of clinically relevant pancreatic fistula after pancreatic surgery using preoperative CT scan: A systematic review and meta-analysis.

Background: Body composition analysis has emerged as a practical tool for predicting outcomes following pancreatic surgery. However, the impact of body composition disorders on clinically relevant postoperative pancreatic fistula (CR-POPF) remains inconclusive. The aim of this study was to review and analyse whether radiographically assessed body composition is predictive of CR-POPF.

Radiomics of rectal cancer for predicting distant metastasis and overall survival.

Background: Rectal cancer (RC) patient stratification by different factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification, personalized treatment, and prognostication of RC patients.

Aim: To build a novel model for predicting the presence of distant metastases and 3-year overall survival (OS) in RC patients.

Multiparametric radiomics nomogram may be used for predicting the severity of esophageal varices in cirrhotic patients.

Background: To explore whether a multiparametric radiomics nomogram on computed tomography (CT) images based on radiomics and relevant parameters of esophageal varices (EV) can be used for predicting the EV severity in patients with cirrhotic livers.

Methods: From January 2016 to August 2018, 136 consecutive patients with clinicopathologically confirmed liver cirrhosis were included for the development of a predictive model. The patients were then divided into two groups, including non-conspicuous EV group (mild-to-moderate EV, n=30) and conspicuous EV group (severe EV, n=106) by using the endoscopic validation as the reference standard.