This study is an extension of our previously published manuscript [2]. Building upon the foundations laid in our earlier work, this research further refines and extends the transformer-based approach to enhance its diagnostic capabilities. The focus remains on the integration of multi-modal information, combining both textual clinical narratives and imaging data from multi slice CT scans, to provide a more comprehensive and accurate diagnosis of brain strokes. In addition, we introduce a new ensemble learning approach based on logic gates. This innovative method combines information from clinical narratives and CT scans to enhance the diagnostic capabilities of our framework, marking a significant evolution in our approach to advancing precision in brain stroke detection.
We discuss a generalization of geometric algebras known as ternary Clifford algebras. In these objects, we have a fixed ternary form instead of a quadratic form as in ordinary geometric algebras. Basis-free definitions of the determinant, trace, and characteristic polynomial in ternary Clifford algebra are introduced. Explicit formulas are presented for all coefficients of the characteristic polynomial and inverse in ternary Clifford algebra. The operation of Hermitian transpose (Hermitian conjugation) in ternary Clifford algebra is introduced without using the corresponding matrix representation. We present a natural realization of the unitary Lie group SU(3) , which is important for physical applications, using only operations in ternary Clifford algebra. An explicit basis of the corresponding Lie algebra 𝔰𝔲(3) is presented. We present an explicit connection with the well-known Gell-Mann basis of 𝔰𝔲(3) . The results can be used in physics, computer science, and engineering.
The goal of this research is to identify surgical movements using RGB videos. Prior methods for recognizing surgical gestures mainly concentrate on robot-assisted surgery, but less on surgical gesture training. In this paper, we introduce a novel surgical gesture dataset that comprises the human surgical gestures of experienced surgeons captured from multiple viewpoints. Moreover, we propose a spatial-temporal transformer-based surgical gesture recognition model called TransSG to identify the user’s surgical gestures better. Our framework comprises three key elements: (1) the importance-weight layer that learns the significance of segmented patches before passing them to the spatial encoder, (2) the aggregated temporal feature extractor that facilitates computationally efficient processing, and (3) a novel loss function that integrates a range of effective loss functions to enhance recognition quality. Our experiments demonstrate that TransSG attains excellent Top-1 accuracy (83.6
To capture 3D geometric details, point cloud understanding primarily employs convolution, dynamic map, or transformer methods to build complex local geometry extractors. When dealing with sparse and noisy point clouds in real-world environments, the structural design of these methods tends to be complex and perform poorly. In this paper, we propose a lightweight point cloud understanding framework based on Laplacian feature convolution, which has high accuracy on normal-sized point cloud datasets and performs well when dealing with sparse and noisy point cloud inputs. The adaptive Laplacian convolution module combined with the vector attention mechanism can effectively capture the local details of point cloud geometry. The framework employs a multi-scale feature fusion mechanism between modules to reduce the number of sampling points step by step, reducing redundant computations and achieving a lightweight effect. In addition, we propose a parameter-free module for interpolation to enhance the training part. Experimental results on object-level datasets show that the lightweight method proposed in our paper has similar accuracy to the current best method in terms of accuracy (Acc) and mean intersection over union (mIoU), and has better robustness in handling sparse and noisy point clouds.
