Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection-Reference-Cited by-同舟云学术

Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

Published:2022 Issue: Volume: Page:14-23
ISSN:0302-9743
Container-title:Deep Generative Models
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Author:

Patel Ashay^ORCID,Tudosiu Petru-Daniel^ORCID,Pinaya Walter Hugo Lopez^ORCID,Cook Gary^ORCID,Goh Vicky^ORCID,Ourselin Sebastien^ORCID,Cardoso M. Jorge^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-18576-2_2

Reference27 articles.

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2. Baur, C., Denner, S., Wiestler, B., Albarqouni, S., Navab, N.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study (2020)

3. Burgos, N., et al.: Anomaly detection for the individual analysis of brain pet images. J. Med. Imaging (Bellingham, Wash.) 8, 024003 (2021). https://doi.org/10.1117/1.JMI.8.2.024003

4. Chen, C.F., Fan, Q., Panda, R.: CrossViT: cross-attention multi-scale vision transformer for image classification (2021)

5. Chen, M., Radford, A., Wu, J., Heewoo, J., Dhariwal, P.: Generative pretraining from pixels (2020)

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