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关键词： 3D Gaussian Splatting   Raster multi-view CAD sketch   Self-supervised learning   Sparse-view reconstruction  

摘要： The automatic reconstruction of 3D computer-aided design (CAD) models from CAD sketches has recently gained significant attention in the computer vision community. Most existing methods, however, rely on vector CAD sketches and 3D ground truth for supervision, which are often difficult to be obtained in industrial applications and are sensitive to noisy inputs. We propose viewing CAD reconstruction as a specific instance of sparse-view 3D reconstruction to overcome these limitations. While this reformulation offers a promising perspective, existing 3D reconstruction methods typically require natural images and corresponding camera poses as inputs, which introduces two major significant challenges: (1) modality discrepancy between CAD sketches and natural images, and (2) difficulty of accurate camera pose estimation for CAD sketches. To solve these issues, we first transform the CAD sketches into representations resembling natural images and extract corresponding masks. Next, we manually calculate the camera poses for the orthographic views to ensure accurate alignment within the 3D coordinate system. Finally, we employ a customized sparse-view 3D reconstruction method to achieve high-quality reconstructions from aligned orthographic views. By leveraging raster CAD sketches for self-supervision, our approach eliminates the reliance on vector CAD sketches and 3D ground truth. Experiments on the Sub-Fusion360 dataset and several cases on other types of CAD sketches demonstrate that our proposed method significantly outperforms previous approaches in CAD reconstruction performance and exhibits strong robustness to noisy inputs. The source code and all related data will be released upon acceptance of the paper.

关键词： Channel-wise joint disentanglement representation learning   Computer-Aided Diagnosis   Gradient-guided disentanglement   Gramian-based Contrastive Learning Disentanglement   Super-resolution ultrasound  

摘要： B-mode ultrasound (BUS) is widely used in breast cancer diagnosis, while the emerging super-resolution ultrasound (SRUS) provides microvascular information with high spatial resolution, which has shown great potential in improving breast cancer diagnosis. However, as a new ultrasound modality, its diagnosis remains highly dependent on the clinical experience of sonologists, highlighting the need for reliable computer-aided diagnosis (CAD) approaches. In this work, a novel dual-branch network with a Channel-Wise Joint Disentanglement Representation Learning (CW-JDRL) method is proposed for the multimodal ultrasound-based CAD of breast cancer, where one branch processes BUS and the other analyzes multimodal SRUS data. The CW-JDRL is implemented on the SRUS branch by grouping the final-layer network channels to capture both common and specific properties. It consists of two modules, namely Gradient-guided Disentanglement (GD) module and Gramian-based Contrastive Learning Disentanglement (GCLD) module. The former disentangles with gradient guidance to encourage consistency among common channels and distinctiveness among specific ones, and the latter disentangles common and specific representations by integrating them into a unified contrastive objective. Extensive experiments on a multicenter SRUS dataset demonstrate that the proposed dual-branch network with CW-JDRL achieves superior performance over the compared algorithms and maintains robust generalizability to external data. It suggests not only the effectiveness of SRUS for diagnosis of breast cancer, but also the potential of the proposed CAD model in clinical practice. The codes are publicly available at https://***/Zyh-AIUltra/CW-JDRL#

关键词： Closed-form solution   Convex cam profile   Load simulator   Parameters optimization   Wrapping cams  

摘要： Reproducing the aerodynamic torque during the deployment process is essential for the ground verification of the folding wing actuation system. However, the existing load simulators rely on high-power, high-performance hardware and complex control algorithms to achieve rapid tracking of high-magnitude torque, which makes it difficult to quickly deploy in harsh test environments and adapt to different test objects. We present a passive-compliance wrapping-cam load simulator with simple structure, rapid response, and high load capacity and establish an efficient multi-scenario design methodology for diverse load curves. A convexity-embedded cam-profile parametric model is innovatively established during optimization-based design, which preserves satisfactory profile description ability and markedly improved optimization efficiency. A new closed-form solution for wrapping cam synthesis is developed, along with an efficient and versatile hybrid design workflow for the load simulator. The proposed design method was tested using simulations of several representative load cases and was verified on the prototype test bench. Under the sinusoidal target load, the corresponding load simulator achieved good loading accuracy with a relative error of less than 5% and R²>0.99 at various deployment speeds.

摘要： We thank the author(s) of the letter entitled Comment on: Clinical behaviour and complications of CAD-CAM subperiosteal implants supporting fixed partial restorations: a scoping review for their careful reading of our work and for their valuable a...

关键词： Pancreatic ductal adenocarcinoma   ADC   Immunotherapy   CAF   Macrophages  

摘要： Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with poor 5-year overall survival. This can be mainly explained by late diagnosis mostly at advanced stages, excluding primary tumor resection as putative curative treatment, and a profound therapy resistance. L1 cell adhesion molecule (L1CAM) is upregulated in PDAC cells and associated with different hallmarks of cancer contributing to tumor progression and therapy resistance. Using L1CAM as tumor associated antigen, two antibody drug conjugates (ADC) targeting L1CAM were developed with different toxins as payload. Both L1CAM-ADC led to a L1CAM specific reduction in tumor cell growth of up to 60% in 2D PDAC cell models. A maximum therapeutic effect was observed when at least 50% of the PDAC cells expressed L1CAM. In 3D PDAC spheroids, MMAE conjugated L1CAM-ADC still showed strong therapeutic effects, while efficacy of SG3199 conjugated L1CAM-ADC was diminished. Co-culture of PDAC spheroids with pancreatic myofibroblasts did almost not impair L1CAM-ADC anti-tumor effects, while macrophages enhanced anti-tumor responses. Application of L1CAM-ADC as adjuvant or palliative treatment in clinically adapted PDAC mouse models exerted potent anti-tumor effects even with enhanced tumor burden. Adjuvantly applied, MMAE conjugated L1CAM-ADC efficiently reduced pancreatic relapses and peritoneal metastases, whereas SG3199 conjugated L1CAM-ADC less impacted pancreatic relapses, but almost completely eliminated liver and peritoneal metastases. In the palliative setting, both L1CAM-ADC reduced number and size of liver and peritoneal metastases. Overall, our results demonstrate strong anti-tumor effects of L1CAM-ADC underscoring their potential as immunotherapeutic approach in therapy of even advanced PDAC.

关键词： SAM   Segmentation   Ultrasound images   Weakly supervised learning  

摘要： Ultrasound imaging is vital for the early detection of breast cancer, where accurate lesion segmentation supports clinical diagnosis and treatment planning. However, existing deep learning-based methods rely on pixel-level annotations, which are costly and labor-intensive to obtain. This study presents a weakly supervised framework for breast lesion segmentation in ultrasound images. The framework combines morphological enhancement with Class Activation Map (CAM)-guided lesion localization and utilizes the Segment Anything Model (SAM) for refined segmentation without pixel-level labels. By adopting a lightweight region synthesis strategy and relying solely on SAM inference, the proposed approach substantially reduces model complexity and computational cost while maintaining high segmentation accuracy. Experimental results on the BUSI dataset show that our method achieves a Dice coefficient of 0.7063 under five-fold cross-validation and outperforms several fully supervised models in Hausdorff distance metrics. These results demonstrate that the proposed framework effectively balances segmentation accuracy, computational efficiency, and annotation cost, offering a practical and low-complexity solution for breast ultrasound analysis. The code for this study is available at: https://***/YueXin18/MorSeg-CAM-SAM-Segmentation.

关键词： Parameterised NC codes   Optimised free-form milling   Tool wear   Form error   Forced vibration  

摘要： Ball-end milling is a fundamental process for machining free-form geometries, typically executed via parallel toolpaths over curved surfaces. Due to the continuous variation in tool-surface engagement, the effective tool diameter and cutting speed (m/min) alter, affecting the process. This study introduces CAMSpeed, a novel software routine designed to dynamically parameterise spindle speed (RPM) and feed rate (mm/min) based on real-time tool-surface contact. The goal is to maintain a consistent cutting speed and feed per tooth along the entire free-form toolpath. CAMSpeed was successfully integrated into a commercial CAD/CAM platform, and milling experiments were conducted to validate the approach. Results demonstrated that the use of parameterised NC codes reduced machining time by 28% and form error by 8%, without any increase in tool wear. Furthermore, modal analysis confirmed that the continuous variation of spindle speed did not induce machining-related forced vibrations. These findings underscore the potential of the proposed algorithm to optimise free-form milling by adaptively adjusting spindle speed and feed rate in response to geometrical complexity.

摘要： Sir, The recently published scoping review by Ruiz-Rincón et al that evaluates the clinical behaviour and complications of CAD-CAM subperiosteal implants (SPIs) supporting fixed partial restorations, provides a timely and focused synthesis of an e...

摘要： I read with interest the article by Priyadarshini et al. addressing complementary and alternative medicine (CAM) use among children with acute viral hepatitis (AVH), an important and understudied issue in pediatric practice. The prospective design...