865 research outputs found
Spherical Transformer: Adapting Spherical Signal to CNNs
Convolutional neural networks (CNNs) have been widely used in various vision
tasks, e.g. image classification, semantic segmentation, etc. Unfortunately,
standard 2D CNNs are not well suited for spherical signals such as panorama
images or spherical projections, as the sphere is an unstructured grid. In this
paper, we present Spherical Transformer which can transform spherical signals
into vectors that can be directly processed by standard CNNs such that many
well-designed CNNs architectures can be reused across tasks and datasets by
pretraining. To this end, the proposed method first uses locally structured
sampling methods such as HEALPix to construct a transformer grid by using the
information of spherical points and its adjacent points, and then transforms
the spherical signals to the vectors through the grid. By building the
Spherical Transformer module, we can use multiple CNN architectures directly.
We evaluate our approach on the tasks of spherical MNIST recognition, 3D object
classification and omnidirectional image semantic segmentation. For 3D object
classification, we further propose a rendering-based projection method to
improve the performance and a rotational-equivariant model to improve the
anti-rotation ability. Experimental results on three tasks show that our
approach achieves superior performance over state-of-the-art methods
Fast Hybrid Cascade for Voxel-based 3D Object Classification
Voxel-based 3D object classification has been frequently studied in recent
years. The previous methods often directly convert the classic 2D convolution
into a 3D form applied to an object with binary voxel representation. In this
paper, we investigate the reason why binary voxel representation is not very
suitable for 3D convolution and how to simultaneously improve the performance
both in accuracy and speed. We show that by giving each voxel a signed distance
value, the accuracy will gain about 30% promotion compared with binary voxel
representation using a two-layer fully connected network. We then propose a
fast fully connected and convolution hybrid cascade network for voxel-based 3D
object classification. This threestage cascade network can divide 3D models
into three categories: easy, moderate and hard. Consequently, the mean
inference time (0.3ms) can speedup about 5x and 2x compared with the
state-of-the-art point cloud and voxel based methods respectively, while
achieving the highest accuracy in the latter category of methods (92%).
Experiments with ModelNet andMNIST verify the performance of the proposed
hybrid cascade network
The chasm in percutaneous coronary intervention and in-hospital mortality rates among acute myocardial infarction patients in rural and urban hospitals in China: A mediation analysis
Objectives: To determine to what extent the inequality in the ability to provide percutaneous coronary intervention (PCI) translates into outcomes for AMI patients in China. Methods: We identified 82,677 patients who had primary diagnoses of AMI and were hospitalized in Shanxi Province, China, between 2013 and 2017. We applied logistic regressions with inverse probability weighting based on propensity scores and mediation analyses to examine the association of hospital rurality with in-hospital mortality and the potential mediating effects of PCI. Results: In multivariate models where PCI was not adjusted for, rural hospitals were associated with a significantly higher risk of in-hospital mortality (odds ratio [OR]: 1.19, 95% confidence interval [CI]: 1.03–1.37). However, this association was nullified (OR: 0.94, 95% CI: 0.81–1.08) when PCI was included as a covariate. Mediation analyses revealed that PCI significantly mediated 132.3% (95% CI: 104.1–256.6%) of the effect of hospital rurality on in-hospital mortality. The direct effect of hospital rurality on in-hospital mortality was insignificant. Conclusion: The results highlight the need to improve rural hospitals’ infrastructure and address the inequalities of treatments and outcomes in rural and urban hospitals
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