Rotation-invariant recognition of shapes is a common challenge in computer vision. Recent approaches have significantly improved the accuracy of rotation-invariant recognition by encoding the rotational invariance of shapes as hand-crafted image features and introducing deep neural networks. However, the methods based on pixels have too much redundant information, and the critical geometric information is prone to early leakage, resulting in weak rotation-invariant recognition of fine-grained shapes. In this paper, we reconsider the shape recognition problem from the perspective of contour points rather than pixels. We propose an anti-noise rotation-invariant convolution module based on contour geometric aware for fine-grained shape recognition. The module divides the shape contour into multiple local geometric regions (LGA), where we implement finer-grained rotation-invariant coding in terms of point topological relations. We provide a deep network composed of five such cascaded modules for classification and retrieval experiments. The results show that our method exhibits excellent performance in rotation-invariant recognition of fine-grained shapes. In addition, we demonstrate that our method is robust to contour noise and the rotation centers.

Negative pressure (NP) therapy with sliding suction is an effective method for limb lymphedema. Due to the caregiver shortage and the patients increase, the robotic NP therapeutic system with a variable-sized suction head can be used to help the lymphedema therapy. However, the varying complexity of different limb regions can affect the accuracy of the suction path. Moreover, the moving suction path should maintain smoothness to ensure therapeutic efficacy. Therefore, finding a smooth sliding path with highly accurate suction poses on the unstructured limb surface poses a significant challenge for robotic therapy. In this paper, a smooth sliding path planning method is proposed for robotic continuous suction in limb lymphedema therapy. The easily-sealed region is identified by comparing point normals to the lip’s suction angle, simplifying path planning to a 2D plane due to lip and limb flexibility. The conjugate gradient method optimizes the path with centroid distance and smoothness constraints. Finally, after the generation of suction poses under the constraints of the lip shape, a smooth sliding path along with lip pressure commands, is obtained to regulate the robot in performing continuous suction therapy. In the experiment, the manipulator with a variable-sized head has been used to finish 10 sliding suctions from different planning path. From the result, the robot could complete 6 times sliding suctions on the phantom arm.

With the continuous advancement of minimally invasive surgery, the incisions have become increasingly smaller, leading to a proportional convergence between the physical dimensions of surgical instruments and the operational space. This phenomenon has exacerbated the issue of visual obstruction caused by the overlapping of the instruments, which now stands as a significant technical impediment to the enhancement of precision in minimally invasive procedures. Vanilla approaches rely on deep learning-based image inpainting techniques to address this issue, but their results are unreliable for surgeons making decisions. This work rethinks hardware design, sacrificing resolution to increase the view of cameras, effectively filling the instrument occluded areas through multi-view technology. Subsequently, a super-resolution method is employed to restore the details of the inpainted images. This innovative approach transforms the uncertainty of deep learning from a large-range pixel inference problem into a more controllable pixel interpolation task, thereby significantly enhancing the reliability and accuracy of the repaired results. Taking spinal endoscopic surgery as an application scenario, we designed a miniature multi-view endoscope system tailored to the specific needs of this surgery. Phantom experiments are conducted to verify the feasibility of the proposed instrument transparency method. The results demonstrate the potential of the proposed method for improving minimally invasive surgery.

Precise quantification of protein-ligand interaction is critical in early-stage drug discovery. Artificial intelligence (AI) has gained massive popularity in this area, with deep-learning models used to extract features from ligand and protein molecules. However, these models often fail to capture intermolecular non-covalent interactions, the primary factor influencing binding, leading to lower accuracy and interpretability. Moreover, such models overlook the spatial structure of protein-ligand complexes, resulting in weaker generalization. To address these issues, we propose Non-covalent Interaction-aware Graph Neural Network (NciaNet), a novel method that effectively utilizes intermolecular non-covalent interactions and 3D protein-ligand structure. Our approach achieves excellent predictive performance on multiple benchmark datasets and outperforms competitive baseline models in the binding affinity task, with the benchmark core set v.2016 achieving an RMSE of 1.208 and an R of 0.833, and the core set v.2013 achieving an RMSE of 1.409 and an R of 0.805, under the high-quality refined v.2016 training conditions. Importantly, NciaNet successfully learns vital features related to protein-ligand interactions, providing biochemical insights and demonstrating practical utility and reliability.