Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution-Reference-Cited by-同舟云学术

Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution

Published:2023-01 Issue: Volume: Page:
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Container-title:2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Georgescu Mariana-Iuliana¹,Ionescu Radu Tudor¹,Miron Andreea-Iuliana²,Savencu Olivian²,Ristea Nicolae-Catalin¹,Verga Nicolae²,Khan Fahad Shahbaz³

Affiliation:

1. University of Bucharest,Romania

2. "Carol Davila" University of Medicine and Pharmacy,Romania

3. MBZ University of Artificial Intelligence,UAE

Publisher

IEEE

Link

Reference54 articles.

1. UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation;gao;Proceedings of MICCAI,2021

3. Generative adversarial nets;goodfellow;Proceedings of NIPS,2014

5. End-to-end object detection with adaptive clustering transformer;zheng;Proceedings of BMVC,2020

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3. MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical Images;Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing;2023-12-15

5. MATNet: MRI Super-Resolution with Multiple Attention Mechanisms;2023 International Conference on New Trends in Computational Intelligence (NTCI);2023-11-03