SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images-Reference-Cited by-同舟云学术

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SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images

Published:2023-12 Issue:12 Volume:42 Page:3860-3870
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Dong Haoyu¹^ORCID,Zhang Yifan²,Gu Hanxue¹^ORCID,Konz Nicholas¹^ORCID,Zhang Yixin¹^ORCID,Mazurowski Maciej A.³

Affiliation:

1. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

2. Department of Computer Science, Vanderbilt University, Nashville, TN, USA

3. Department of Radiology, Electrical and Computer Engineering, Biostatistics and Bioinformatics, and Computer Science, Duke University, Durham, NC, USA

Funder

National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10336247/10247020.pdf?arnumber=10247020

Reference62 articles.

1. Barlow twins: Self-supervised learning via redundancy reduction;zbontar;Proc ICML,2021

2. ImageNet-trained CNNs are biased towards texture; Increasing shape bias improves accuracy and robustness;geirhos;Proc Int Conf Learn Represent,2019

3. StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

4. Computing Receptive Fields of Convolutional Neural Networks

5. Revisiting contrastive methods for unsupervised learning of visual representations;van gansbeke;Proc Adv Neural Inf Process Syst,2021

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