Point cloud registration, a fundamental task in computer science and artificial intelligence, involves rigidly transforming point clouds from different perspectives into a common coordinate system. Traditional registration methods often lack robustness and fail to achieve the desired level of accuracy. In contrast, deep learning-based registration methods have demonstrated improved accuracy and generalization. However, these methods are hindered by large parameter sizes, complex network architectures, and challenges related to efficiency, robustness, and partial overlaps. In this study, we propose a lightweight deep learning-based registration method that captures features from multiple perspectives to predict overlapping points and mitigate the interference of non-overlapping points. Specifically, our approach utilizes pruning and weight-sharing quantization techniques to reduce model size and simplify the network structure. We evaluate the proposed model on noisy and partially overlapping point clouds from the ModelNet40 dataset, comparing its performance against other existing methods. Experimental results show that the proposed method significantly reduces the model's parameter size without compromising registration accuracy.

Human point cloud registration is a critical problem in the fields of computer vision and computer graphics applications. Currently, due to the presence of joint hinges and limb occlusions in human point clouds, point cloud alignment is challenging. To address these two limits, this paper proposes an unsupervised non-rigid human point cloud registration method based on deformation field fusion. The method mainly consists of the deep dynamic link deformation field estimation module and the probabilistic alignment deformation field estimation module. The deep dynamic link deformation field estimation module uses a time series network to convert non-rigid deformation into multiple rigid deformations. Then, feature extraction is performed to estimate the deformation field based on the rigid deformations. The probabilistic alignment deformation field estimation module builds on a Gaussian mixture model and adds local and global constraint conditions for deformation field estimation. Finally, the two deformation fields are fused into the total deformed field by aligning them, which enhances the sensitivity to both global and local feature information. The experimental results on public datasets and real private datasets demonstrate that the proposed method has higher accuracy and better robustness under joint hinges and limb adhesion conditions.

In recent years, CNN-Transformer hybrid networks, particularly those with dual-stream pyramid encoders, have gained significant attention in medical image analysis for their ability to combine the strengths of both CNNs and Transformers. However, a critical issue has been largely overlooked: the initial misalignment or inaccurate pre-alignment of image pairs, which can degrade the performance of these networks and limit their effectiveness on clinical datasets. To address this, we propose a novel multi-scale network framework for joint affine and deformable image registration tasks. Our approach introduces an additional branch to the dual-stream pyramid encoder, utilizing the Feature Selector to establish more precise spatial mappings for initially misaligned image pairs. This improves the network’s ability to correct large spatial misalignments during joint affine registration. Additionally, the Feature Translator in the decoder captures regions of interest based on image intensity, leveraging the inductive biases of convolutional layers to enhance pixel-level alignment. Our model demonstrates robust performance on registration tasks using two publicly available brain MRI datasets and is effective in addressing inter-frame mismatches in brain PET images. The code for our framework will be publicly available at https://github.com/ZZU-zy228/FSTNet.

In this article, glioma segmentation in the glioma grading computer-aided diagnosis (CAD) system requires manual delineation from radiologists, adding substantially to their workload. Although automatic segmentation is powerful, it cannot fully delegate power to artificial intelligence. We propose an AI-powered radiomics algorithm based on slice pooling (AI-RASP). AI-RASP generated compress images by compressing the gray value of each magnetic resonance imaging slice for radiologists to segment manually. In addition, AI-RASP integrated radiomics models to verify the glioma grading effect and the availability of compressed images. AI-RASP significantly reduce the time of manual segmentation. Results reported on multicenter datasets reveal that our architecture is better than the traditional manual segmentation while being over five times faster. The radiomics model with slice pooling mechanism achieves an area under the curve values of 0.86, 086, and 0.83 in the validation cohorts. Radiologists and patients can benefit from a CAD system integrated with AI-RASP.

A Multi-Label Problem Transformation Joint Classification (MLPTJC) method is developed to solve the multi-label classification problem for the health and disease risk prediction based on physical examination records. We adopt a multi-class classification problem transformation method to transform the multi-label classification problem to a multi-class classification problem. Then We propose a Joint Decomposition Subset Classifier method to reduce the infrequent label sets to deal with the imbalance learning problem. Based on MLPTJC, existing cost-sensitive multi-class classification algorithms can be used to train the prediction models. We conduct some experiments to evaluate the performance of the MLPTJC method. The Support Vector Machine (SVM) and Random Forest (RF) algorithms are used for multi-class classification learning. We use the 10-fold cross-validation and metrics such as Average Accuracy, Precision, Recall and F-measure to evaluate the performance. The real physical examination records were employed, which include 62 examination items and 110, 300 anonymous patients. 8 types of diseases were predicted. The experimental results show that the MLPTJC method has better performance in terms of accuracy.

The traditional multicast model has some problems, such as access control, address allocation and protocol extensibility. To solve these problems, we provide a new group communication model named MixCast, which is suitable for large-scale heterogeneous networks. MixCast uses unicast between different access networks and use multicast in the same access network; at the same time, MixCast uses IEEE 802.1X protocol to provide user access control and billing scheme for carriers. We also analyze the cost of MixCast communication in the network equipments.