Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning

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Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning
Title:
Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning
Other Titles:
2021 IEEE International Conference on Image Processing (ICIP)
Publication Date:
23 August 2021
Citation:
Huang, L., Cheng, D., Yang, X., Lin, T., Shi, Y., Yang, K., … Wen, B. (2021). Joint Anomaly Detection and Inpainting for Microscopy Images Via Deep Self-Supervised Learning. 2021 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip42928.2021.9506454
Abstract:
While microscopy enables material scientists to view and analyze microstructures, the imaging results often include defects and anomalies with varied shapes and locations. The presence of such anomalies significantly degrades the quality of microscopy images and the subsequent analytical tasks. Comparing to classic feature-based methods, recent advancements in deep learning provide a more efficient, accurate, and scalable approach to detect and remove anomalies in microscopy images. However, most of the deep inpainting and anomaly detection schemes require a certain level of supervision, i.e., either annotation of the anomalies, or a corpus of purely normal data, which are limited in practice for supervision-starving microscopy applications. In this work, we propose a self-supervised deep learning scheme for joint anomaly detection and inpainting of microscopy images. The proposed anomaly detection model can be trained over a mixture of normal and abnormal microscopy images without any labeling. Instead of a two-stage scheme, our multi-task model can simultaneously detect abnormal regions and remove the defects via jointly training. To benchmark such microscopy application under the real-world setup, we propose a novel dataset of real microscopic images of integrated circuits, dubbed MIIC. The proposed dataset contains tens of thousands of normal microscopic images, while we labeled hundreds of them containing various imaging and manufacturing anomalies and defects for testing. Experiments show that the proposed model outperforms various popular or state-of-the-art competing methods for both microscopy image anomaly detection and inpainting.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
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