A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective

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

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”

Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion

Radar is ubiquitous in autonomous driving systems due to its low cost and good adaptability to bad weather. Nevertheless, the radar detection performance is usually inferior because its point cloud is sparse and not accurate due to the poor azimuth and elevation resolution. Moreover, point cloud generation algorithms already drop weak signals to reduce the false targets which may be suboptimal for the use of deep fusion. In this paper, we propose a novel method named EchoFusion to skip the existing radar signal processing pipeline and then incorporate the radar raw data with other sensors. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors. By this approach, our method could utilize both rich and lossless distance and speed clues from radar echoes and rich semantic clues from images, making our method surpass all existing methods on the RADIal dataset, and approach the performance of LiDAR. The code will be released on https://github.com/tusen-ai/EchoFusion.Comment: Accepted by NeurIPS 202