322 research outputs found

    FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection

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    The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection. Recent approaches mainly focus on the design of view transformation, either pixel-wisely lifting perspective view features into 3D space with estimated depth or grid-wisely constructing BEV features via 3D projection, treating all pixels or grids equally. However, choosing what to transform is also important but has rarely been discussed before. The pixels of a moving car are more informative than the pixels of the sky. To fully utilize the information contained in images, the view transformation should be able to adapt to different image regions according to their contents. In this paper, we propose a novel framework named FrustumFormer, which pays more attention to the features in instance regions via adaptive instance-aware resampling. Specifically, the model obtains instance frustums on the bird's eye view by leveraging image view object proposals. An adaptive occupancy mask within the instance frustum is learned to refine the instance location. Moreover, the temporal frustum intersection could further reduce the localization uncertainty of objects. Comprehensive experiments on the nuScenes dataset demonstrate the effectiveness of FrustumFormer, and we achieve a new state-of-the-art performance on the benchmark. Codes and models will be made available at https://github.com/Robertwyq/Frustum.Comment: Accepted to CVPR 202

    Undifferentiated High-grade Pleomorphic Sarcoma (Malignant Fibrous Histiocytoma ) Occurring in the Nerve Root: A Rare Case Report and Review of the Literature

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    Introduction: undifferentiated pleomorphic sarcoma (UPS) represents a group of pleomorphic mesenchymal neoplasms without any defined cell differentiation, occurs more commonly in the extremities. However, we report a rare case of UPS, not malignant peripheral nerve sheath tumor (MPNST) in which the nerve root of the forth cervical vertebrae and adjacent tissues were involved. Presentation of Case: Histopathologically, this tumor was composed of highly atypical spindle cells, pleomorphic cells and multinucleated giant cells. Nuclear mitoses were frequently observed. Immunohistochemistrical results showed that the tumor cells stained positively for vimentin but negatively for all the other immunomarkers.Conclusion: We here reported an extremely rare case of UPS arising from the nerve root of the forth cervical vertebrae and proposed a hypothesis “tumors without any expression of neural markers should be diagnosed as UPSs, not MPNSTs, even though which may arise from peripheral nerve branches”

    4D Unsupervised Object Discovery

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    Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. Therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data -- 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly iteratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones. Codes and models will be made available at https://github.com/Robertwyq/LSMOL.Comment: Accepted by NeurIPS 2022. 17 pages, 6 figure

    Are acupoints specific for diseases? A systematic review of the randomized controlled trials with sham acupuncture controls

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    <p>Abstract</p> <p>Background</p> <p>The results of many clinical trials and experimental studies regarding acupoint specificity are contradictory. This review aims to investigate whether a difference in efficacy exists between ordinary acupuncture on specific acupoints and sham acupuncture controls on non-acupoints or on irrelevant acupoints.</p> <p>Methods</p> <p>Databases including Medline, Embase, AMED and Chinese Biomedical Database were searched to identify randomized controlled trials published between 1998 and 2009 that compared traditional body acupuncture on acupoints with sham acupuncture controls on irrelevant acupoints or non-acupoints with the same needling depth. The Cochrane Collaboration's tool for assessing risk of bias was employed to address the quality of the included trials.</p> <p>Results</p> <p>Twelve acupuncture clinical trials with sham acupuncture controls were identified and included in the review. The conditions treated varied. Half of the included trials had positive results on the primary outcomes and demonstrated acupoint specificity. However, among those six trials (total sample size: 985) with low risk of bias, five trials (sample size: 940) showed no statistically significant difference between proper and sham acupuncture treatments.</p> <p>Conclusion</p> <p>This review did not demonstrate the existence of acupoint specificity. Further clinical trials with larger sample sizes, optimal acupuncture treatment protocols and appropriate sham acupuncture controls are required to resolve this important issue.</p
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