Self-Supervised Surface Defect Localization via Joint De-Anomaly Reconstruction and Saliency-Guided Segmentation

Yang, Huixin; Zhu, Zhenfeng; Lin, Chen; Hui, Wing-Chi; Wang, Shenghui; Zhao, Yao

doi:10.1109/tim.2023.3273681

Cited by 6 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most state-of-the-art works realize AD by building selfsupervised tasks on the training dataset, which mainly include sample reconstruction [4]- [22], pseudo-outlier augmentation [23]- [26], and knowledge distillation (KD) [27]- [33]. Previous KD-based frameworks usually use a sufficiently pretrained teacher network (T-Net) and a student network (S-Net) with a similar or identical structure to form a teacherstudent (T-S) model.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly Consistency

Liu,

Jiao,

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Anomaly detection (AD) in medical images aims to recognize test-time abnormal inputs according to normal samples in the training set. Knowledge distillation based on the teacher-student (T-S) model is a simple and effective method to identify anomalies, yet its efficacy is constrained by the similarity between teacher and student network architectures. To address this problem, in this paper, we propose a T-S model with skip connections (Skip-TS) which is trained by direct reverse knowledge distillation (DRKD) for AD in medical images. First, to overcome the low sensitivity to anomalies caused by structural similarity, we design an encoder-decoder architecture where the teacher network (T-Net) is a pre-trained encoder and the student network (S-Net) is a randomly initialized decoder. During training, the S-Net learns to reconstruct the shallow representations of images from the output of the T-Net, which is called DRKD. Secondly, we introduce skip connections to the T-S model to prevent the S-Net from missing normal information of images at multi-scale. In addition, we design a multi-scale anomaly consistency (MAC) loss to improve the anomaly detection and localization performance. Thorough experiments conducted on twelve public medical datasets and two private medical datasets demonstrate that our approach surpasses the current state-of-the-art by 6.4% and 8.2% in terms of AUROC on public and private datasets, respectively. Code and organized benchmark datasets will be available at https://github.com/Arktis2022/Skip-TS.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly Consistency

Liu,

Jiao,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Li,

Ren,

et al. 2024

2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)

View full text Add to dashboard Cite

Visual Saliency Approaches for Defect Detection in Industrial Processes: A Short Review

Porta

2024

2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)

View full text Add to dashboard Cite

Self-Supervised Surface Defect Localization via Joint De-Anomaly Reconstruction and Saliency-Guided Segmentation

Cited by 6 publications

References 26 publications

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly Consistency

Anomaly Detection for Medical Images Using Teacher-Student Model with Skip Connections and Multi-scale Anomaly Consistency

Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Visual Saliency Approaches for Defect Detection in Industrial Processes: A Short Review

Contact Info

Product

Resources

About