Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain-Reference-Cited by-同舟云学术

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Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain

Published:2024-08-24 Issue: Volume: Page:1348-1359
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Karimi Monsefi Amin¹^ORCID,Karisani Payam²^ORCID,Zhou Mengxi¹^ORCID,Choi Stacey³^ORCID,Doble Nathan³^ORCID,Ji Heng²^ORCID,Parthasarathy Srinivasan¹^ORCID,Ramnath Rajiv¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

2. Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA

3. Department of Ophthalmology and Visual Science, The Ohio State University, Columbus, OH, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672069

Reference53 articles.

1. Bobby Azad, Reza Azad, Sania Eskandari, and other. 2023. Foundational models in medical imaging: A comprehensive survey and future vision. arXiv preprint arXiv:2310.18689 (2023).

2. Shekoofeh Azizi et al. 2021. Big self-supervised models advance medical image classification. In Proceedings of the IEEE/CVF international conference on computer vision. ICCV.

3. Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

4. MA-Unet: an improved version of Unet based on multi-scale and attention mechanism for medical image segmentation

5. Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis

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