We demonstrate that our federated self-supervised pre-training approaches yield models with superior generalization to unseen data and superior fine-tuning performance with a restricted labeled dataset, as opposed to the existing federated learning approaches. The source code is accessible on GitHub at https://github.com/rui-yan/SSL-FL.
Low-intensity ultrasound (LIUS) treatments are investigated for their capacity to modify the transmission of motor signals in the spinal cord.
Subjects for this study were 10 male Sprague-Dawley rats, at 15 weeks old, weighing within the range of 250-300 grams. Scabiosa comosa Fisch ex Roem et Schult Isoflurane, at a concentration of 2%, was used in conjunction with oxygen flowing at 4 liters per minute via a nasal cannula to induce anesthesia. The process of electrode placement included the cranial, upper extremity, and lower extremity areas. Surgical exposure of the spinal cord at the T11 and T12 vertebral levels was achieved through a thoracic laminectomy. Motor evoked potentials (MEPs), acquired each minute, were obtained from the exposed spinal cord, which was coupled to a LIUS transducer, during either a five-minute or a ten-minute sonication. Following sonication, the ultrasound was deactivated, and post-sonication motor evoked potentials were acquired for five additional minutes.
The 5-minute (p<0.0001) and 10-minute (p=0.0004) groups showed a substantial reduction in hindlimb MEP amplitude during sonication, followed by a steady recovery to baseline readings. During both the 5-minute and 10-minute sonication periods, no statistically significant variation in forelimb motor evoked potential (MEP) amplitude was detected; p-values of 0.46 and 0.80 respectively confirmed this.
LIUS intervention on the spinal cord suppresses motor-evoked potentials (MEPs) situated caudal to the location of the sonication, with subsequent restoration of MEPs to baseline values.
LIUS's capacity to quell spinal motor signals may prove beneficial in addressing movement disorders arising from excessive spinal neuron stimulation.
The suppression of motor signals in the spinal cord by LIUS could be a promising therapeutic strategy for movement disorders triggered by overactive spinal neurons.
This paper's goal is to develop an unsupervised method for learning dense 3D shape correspondence in topologically diverse, generic objects. Given a shape latent code, conventional implicit functions ascertain the occupancy of a 3D point. Each 3D point in the part embedding space is instead represented by a probabilistic embedding, produced by our novel implicit function. Dense correspondence is implemented by using an inverse function that maps part embedding vectors to matching 3D points, provided the corresponding points possess similar embeddings. The shape latent code is generated by the encoder, and both functions are jointly learned with several effective and uncertainty-aware loss functions, this process satisfying our assumption. In the inference process, should the user mark an arbitrary point on the originating form, our algorithm delivers a confidence rating about the presence of a matching point on the resultant form, and the related semantic value if ascertained. With diverse part compositions, man-made objects are inherently benefited by this mechanism. Demonstrating the efficacy of our approach involves unsupervised 3D semantic correspondence and shape segmentation.
Semi-supervised semantic segmentation entails training a semantic segmentation model using a limited dataset of labeled images and a rich dataset of unlabeled images. The method for attaining reliable pseudo-labels for the unlabeled images determines the efficacy of this task. Existing techniques primarily focus on creating reliable pseudo-labels using the confidence scores of unlabeled images, while disregarding the significant contribution of properly annotated labeled images. For semi-supervised semantic segmentation, this paper proposes a Cross-Image Semantic Consistency guided Rectifying (CISC-R) approach that directly uses labeled images to correct pseudo-labels. Our CISC-R's conceptual underpinning rests on the observation that images in the same category demonstrate substantial pixel-level correlation. The initial pseudo-labels provide a starting point for finding a labeled image that contains the same semantic information as the given unlabeled image. We then ascertain the pixel-wise similarity between the unlabeled image and the targeted labeled image, generating a CISC map that facilitates a precise pixel-level rectification of the pseudo-labels. Experiments on the PASCAL VOC 2012, Cityscapes, and COCO datasets provide compelling evidence that the CISC-R method demonstrably enhances the quality of pseudo labels, surpassing the performance of current state-of-the-art models. Code for the CISC-R system is publicly available on GitHub, at https://github.com/Luffy03/CISC-R.
Whether transformer architectures can enhance the capabilities of established convolutional neural networks is presently unknown. Past endeavors have interwoven convolutional and transformer architectures in sequential configurations, but this paper's key contribution lies in the examination of a parallel architectural design. Transforming previous approaches, which necessitated image segmentation into patch-wise tokens, we find multi-head self-attention on convolutional features predominantly responsive to global correlations, with performance declining when these connections are not present. Two parallel modules are suggested, alongside multi-head self-attention, to effectively augment the transformer's performance. For local information retrieval, a dynamic local enhancement module uses convolution to dynamically boost the response of positive local patches and diminish the response of less informative patches. A novel unary co-occurrence excitation module, applied to mid-level structures, actively employs convolution to ascertain the co-occurrence relationships among local patches. A deep architecture, incorporating aggregated parallel Dynamic Unary Convolution (DUCT) blocks, is evaluated across core computer vision tasks, such as image-based classification, segmentation, retrieval, and density estimation, within the Transformer framework. The dynamic and unary convolution employed in our parallel convolutional-transformer approach yields superior results compared to existing series-designed structures, as confirmed by both qualitative and quantitative analyses.
One can readily utilize Fisher's linear discriminant analysis (LDA) for supervised dimensionality reduction tasks. LDA's approach might prove inadequate in scenarios involving intricate class distributions. Deep feedforward neural networks, which incorporate rectified linear units as activation functions, have the capability of mapping multiple input neighborhoods to comparable output states via a progression of spatial folding operations. RepSox This study, contained within this brief paper, illustrates the capability of space-folding to uncover LDA classification details present in subspaces that are inaccessible to conventional LDA techniques. LDA, when combined with space-folding, exhibits superior capacity for extracting classification information than LDA alone. Further development of that composition is attainable by utilizing end-to-end fine-tuning. Empirical findings from experiments conducted on both simulated and publicly accessible datasets validated the viability of the suggested methodology.
The novel localized simple multiple kernel k-means (SimpleMKKM) algorithm establishes an efficient clustering approach, sufficiently accounting for variations across the dataset's samples. Although it outperforms in clustering in some applications, a hyperparameter is needed, pre-determining the size of the localization zone. Implementing this method in real-world scenarios is significantly hindered by the lack of explicit directions for selecting suitable hyperparameters in clustering tasks. To conquer this issue, we initially employ a quadratic combination of pre-calculated fundamental neighborhood mask matrices to parameterize a neighborhood mask matrix, these matrices are linked to a group of hyperparameters. We intend to learn the optimal coefficient for these neighborhood mask matrices concurrently with the clustering process. Employing this method yields the proposed hyperparameter-free localized SimpleMKKM, a more complex minimization-minimization-maximization optimization problem. We recast the optimized output as the minimization of a function representing optimal value, demonstrating its differentiability, and designing a gradient-based method for its calculation. Cell wall biosynthesis Moreover, we theoretically confirm the global optimality of the obtained optimum. The approach's efficacy is proven through comprehensive experimentation across multiple benchmark datasets, contrasting its performance with top methods in the contemporary literature. For access to the hyperparameter-free localized SimpleMKKM's source code, navigate to https//github.com/xinwangliu/SimpleMKKMcodes/.
Glucose metabolism hinges on the pancreas; the removal of the pancreas may lead to the development of diabetes or sustained glucose imbalance as a prevalent sequela. Still, the relative importance of different contributing factors to new-onset diabetes after pancreatectomies remains unclear. The potential of radiomics analysis is its ability to unearth image markers relevant to forecasting or assessing disease. Earlier studies highlighted the superior performance of the integration of imaging and electronic medical records (EMRs), compared to the use of imaging or EMRs in isolation. To discern predictive factors from high-dimensional features is a crucial first step, but the challenge escalates when aiming to choose and synthesize imaging and EMR information. This study presents a radiomics pipeline for evaluating the postoperative risk of new-onset diabetes in patients who have undergone distal pancreatectomy. 3D wavelet transformations are applied to extract multiscale image features, then complemented with clinical data, comprising patient attributes, body composition analysis, and pancreas volume metrics.