Hole based CMOS active pixel sensor for medical x-ray imaging

Jang, Hoon; Park, So Eun; Hwang, Sun Jae; Choi, Chee Hong; Hwang, Joon

doi:10.1109/nssmic.2011.6154321

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Semantic pyramid anomaly detection (SPADE) [63] is inspired by KNN and utilizes correspondences based on a multi-resolution feature pyramid to obtain pixel-level anomaly segmentation results. PaDim [73] employs multivariate Gaussian distributions to construct a probabilistic representation of the normal class. Consequently, the memory bank size is determined solely by the image resolution and not by the size of the training set.…”

Section: Memory Bankmentioning

confidence: 99%

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Deep Industrial Image Anomaly Detection: A Survey

Liu,

Xie,

Wang

et al. 2024

Mach. Intell. Res.

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The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from industrial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

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Section: Memory Bankmentioning

confidence: 99%

“…The second setting relies on the vanilla few-shot image learning. PatchCore [68] , SPADE [63] and PaDim [73] conduct the ablation study on 16 normal training samples. None of them, however, are specialized in few-shot anomaly detection.…”

Section: Few-shot Anomaly Detectionmentioning

confidence: 99%

Deep Industrial Image Anomaly Detection: A Survey

Liu,

Xie,

Wang

et al. 2024

Mach. Intell. Res.

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“…2) Embedding-based methods map normal features to a compact space, distancing them from abnormal parts to ensure discriminative capabilities. Current approaches can be typically categorized into distribution-map-based [14], [47], [48], [49], [50], [51], [52], [53], [54], [55], teacher-student-based [15], [34], [56], [57], [58], [59], and memory-bank-based [16], [60], [61], [62], [63], [64], [65], [66]. 3) Reconstruction-based methods generally consist of an encoder and a decoder, with some approaches incorporating an additional transformation module.…”

Section: Related Workmentioning

confidence: 99%

Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection

Wang

Liu

Chen

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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This work studies the recently proposed challenging and practical Multi-class Unsupervised Anomaly Detection (MUAD) task, which only requires normal images for training while simultaneously testing both normal/anomaly images for multiple classes. Existing reconstruction-based methods typically adopt pyramid networks as encoders/decoders to obtain multi-resolution features, accompanied by elaborate sub-modules with heavier handcraft engineering designs for more precise localization. In contrast, a plain Vision Transformer (ViT) with simple architecture has been shown effective in multiple domains, which is simpler, more effective, and elegant. Following this spirit, this paper explores plain ViT architecture for MUAD. Specifically, we abstract a Meta-AD concept by inducing current reconstruction-based methods. Then, we instantiate a novel and elegant plain ViT-based symmetric ViTAD structure, effectively designed step by step from three macro and four micro perspectives. In addition, this paper reveals several interesting findings for further exploration. Finally, we propose a comprehensive and fair evaluation benchmark on eight metrics for the MUAD task. Based on a naive training recipe, ViTAD achieves state-of-the-art (SoTA) results and efficiency on the MVTec AD and VisA datasets without bells and whistles, obtaining 85.4 mAD that surpasses SoTA UniAD by +3.0↑, and only requiring 1.1 hours and 2.3G GPU memory to complete model training by a single V100 GPU. Source code, models, and more results are available at github.com/zhangzjn/ADer and website.

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