Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Author:
Affiliation:
1. Xiamen University
2. UIUC
3. BUPT
4. University of Hull
5. CAS
6. Hong Kong COCHE
Funder
Natural Science Foundation of Fujian Province
State Key Laboratory of Virtual Reality Technology and Systems
Beihang University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096683.pdf?arnumber=10096683
Reference26 articles.
1. GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-efficient Medical Image Recognition
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3. Semi-wtc: A practical semi-supervised framework for attack categorization through weight-task consistency;li,2022
4. TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
5. Covid mtnet: Covid-19 detection with multi-task deep learning approaches;alom,2020
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1. ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation;IEEE Transactions on Medical Imaging;2024-06
2. LViT: Language Meets Vision Transformer in Medical Image Segmentation;IEEE Transactions on Medical Imaging;2024-01
3. ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26
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