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A point cloud is a simple and concise 3D representation, but point cloud generation is a long-term challenging task in 3D vision. However, most existing methods only focus on their effectiveness of generation and auto-encoding separately. Furthermore, both generative adversarial networks (GANs) and auto-encoders (AEs) are the most popular generative models. But there is a lack of related research that investigates the implicit connections between them in the field of point cloud generation. Thus, we propose a new bidirectional network (BI-Net) trained with collaborative learning, introducing more priors through the alternate parameter optimizations of a GAN and AE combination, which is different from the way of combining them at the network structure and loss function level. Specifically, BI-Net acts as a GAN and AE in different data processing directions, where their network structures can be reused. If optimizing only the GAN without the AE, there is no direct constraint of ground truth on the generator's parameter optimization. This unique approach enables better network optimization and leads to superior generation results. Moreover, we propose a nearest neighbor mutual exclusion (NNME) loss to further homogenize the spatial distribution of generated points during the reverse direction. Extensive experiments were conducted, and the results show that the BI-Net produces competitive and high-quality results on reasonable structure and uniform distributions compared to existing state-of-the-art methods. We believe that our network structure (BI-Net) with collaborative learning could provide a new promising method for future point cloud generation tasks. [ABSTRACT FROM AUTHOR]

Background and Aim Methods Results Conclusions Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time‐consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability.A still image‐driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real‐world applicability, the model was validated using 113 10‐s colonoscopy video clips and 30 full colonoscopy videos to identify “adequate (BBPS 2–3)” or “inadequate (BBPS 0–1)” preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter‐rater variability was analyzed between the AI model and endoscopists.The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively.The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large‐scale screening programs. [ABSTRACT FROM AUTHOR]

Objective: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. Materials and methods: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. Results: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05–329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707–0.971), 0.873 (0–0.997), and 0.894 (0.025–0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). Conclusion: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models. [ABSTRACT FROM AUTHOR]

• The temperature distribution on weldment sidewall exhibits a certain degree of separability under different offset degrees of cladding layer. • A cladding layer offset recognition method based on infrared sensing and deep learning for WAAM is proposed. • The proposed method effectively solves the problem of arc light interference in the cladding layer offset recognition method based on visual perception. In the wire arc additive manufacturing (WAAM) procedure, the stability of the cladding layer's formation directly impacts the final dimensional precision of the WAAM weldment. To achieve high-precision forming of the WAAM weldment, online monitoring of the cladding layer offset is essential. This article explores the monitoring technology for the cladding layer offset in the WAAM process and introduces a cladding layer offset monitoring technique using infrared temperature measurement and deep learning. First, a temperature field measurement system for the WAAM weldment is set up using an infrared thermal imager. Next, a region of interest (ROI) is selected on the weldment sidewall. The optimised histogram of oriented gradient (HOG) algorithm is then employed to extract the temperature distribution characteristics of the ROI. The separability of the temperature field on the weldment sidewall is analysed under various offset degrees of the cladding layer. Finally, using the temperature image of the ROI as input, a cladding layer offset recognition model is developed using a convolutional neural network (CNN). This study also evaluates the effect of the selected ROI position on the recognition precision of the model for the cladding layer offset. Experimental results indicate that the recognition accuracy of the model for cladding layer offset, as presented in this article, exceeds 99%. Moreover, the model demonstrates strong robustness, further enriching the intelligent monitoring methods for the WAAM process. [ABSTRACT FROM AUTHOR]