MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions | Paper Citation Record

Observation 4b48c847-40d5-483e-81b0-8fa13aed87c8 · outbound

This paper cites Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize

Reference 1

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This paper cites Layer nor- malization.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Layer nor- malization

Reference 2

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This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 3

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This paper cites Segmentation labels and radiomic features for the pre-operative scans of the tcga- lgg collection.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Segmentation labels and radiomic features for the pre-operative scans of the tcga- lgg collection

Reference 4

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This paper cites Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features

Reference 5

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This paper cites Bardenfleth, Vedrana A.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Bardenfleth, Vedrana A

Reference 6

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This paper cites Swin-Unet: Unet-like pure transformer for medical image segmentation.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Swin-Unet: Unet-like pure transformer for medical image segmentation

Reference 7

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Observation 0c6e614d-a076-4b31-a524-9245c3caaac4 · outbound

This paper cites End-to- end object detection with transformers.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions End-to- end object detection with transformers

Reference 8

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This paper cites Crossvit: Cross-attention multi-scale vision transformer for image classification.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Crossvit: Cross-attention multi-scale vision transformer for image classification

Reference 9

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Observation 2f4828e2-0cde-4044-aa70-be2fe9022489 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 10

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This paper cites Hat: Hybrid attention transformer for image restoration.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Hat: Hybrid attention transformer for image restoration

Reference 11

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This paper cites Activating more pixels in image super- resolution transformer.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Activating more pixels in image super- resolution transformer

Reference 12

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This paper cites Efficient and ac- curate mri super-resolution using a generative adversar- ial network and 3d multi-level densely connected network.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Efficient and ac- curate mri super-resolution using a generative adversar- ial network and 3d multi-level densely connected network

Reference 13

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This paper cites Brain MRI super resolution using 3D deep densely connected neural networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Brain MRI super resolution using 3D deep densely connected neural networks

Reference 14

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This paper cites MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

Reference 15

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This paper cites HMANet: Hybrid multi-axis aggrega- tion network for image super-resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions HMANet: Hybrid multi-axis aggrega- tion network for image super-resolution

Reference 16

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This paper cites Conde, Ui-Jin Choi, Maxime Burchi, and Radu Timofte.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Conde, Ui-Jin Choi, Maxime Burchi, and Radu Timofte

Reference 17

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This paper cites Image super-resolution using deep convolutional net- works.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Image super-resolution using deep convolutional net- works

Reference 18

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This paper cites Acceler- ating the super-resolution convolutional neural network.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Acceler- ating the super-resolution convolutional neural network

Reference 19

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

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This paper cites Brain MRI super-resolution using 3D dilated convolutional encoder–decoder network.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Brain MRI super-resolution using 3D dilated convolutional encoder–decoder network

Reference 21

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This paper cites Su- perFormer: V olumetric transformer architectures for MRI super-resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Su- perFormer: V olumetric transformer architectures for MRI super-resolution

Reference 22

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This paper cites A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 23

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This paper cites Adamixer: A fast-converging query-based object detector.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Adamixer: A fast-converging query-based object detector

Reference 24

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This paper cites Stereo-correlation and noise-distribution aware ResV oxGAN for dense slices reconstruction and noise reduction in thick low-dose CT.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Stereo-correlation and noise-distribution aware ResV oxGAN for dense slices reconstruction and noise reduction in thick low-dose CT

Reference 25

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This paper cites Interpreting super-resolution networks with local attribution maps.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Interpreting super-resolution networks with local attribution maps

Reference 26

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This paper cites Roth, and Daguang Xu.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Roth, and Daguang Xu

Reference 27

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This paper cites FasterViT: Fast Vision Transformers with Hierarchical Attention.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions FasterViT: Fast Vision Transformers with Hierarchical Attention

Reference 28

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This paper cites Drct: Saving image super-resolution away from informa- tion bottleneck.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Drct: Saving image super-resolution away from informa- tion bottleneck

Reference 29

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MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Weinberger

Reference 30

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This paper cites Deep learning-based magnetic resonance image super-resolution: a survey.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Deep learning-based magnetic resonance image super-resolution: a survey

Reference 31

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This paper cites Deep learning-based magnetic resonance image super-resolution: a survey.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Deep learning-based magnetic resonance image super-resolution: a survey

Reference 32

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This paper cites Ct image super resolution based on improved srgan.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Ct image super resolution based on improved srgan

Reference 33

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This paper cites Adam: A Method for Stochastic Optimization.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Adam: A Method for Stochastic Optimization

Reference 34

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This paper cites Multi- parametric neuroimaging reproducibility: a 3-t resource study.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Multi- parametric neuroimaging reproducibility: a 3-t resource study

Reference 35

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This paper cites Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:32.679127Z

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This paper cites Rethinking multi-contrast mri super-resolution: Rectangle-window cross-attention transformer and arbitrary- scale upsampling.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Rethinking multi-contrast mri super-resolution: Rectangle-window cross-attention transformer and arbitrary- scale upsampling

Reference 37

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This paper cites Multi-level feature extraction and reconstruction for 3d mri image super-resolution.Computers in Biology and Medicine, 171:108151, 2024.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Multi-level feature extraction and reconstruction for 3d mri image super-resolution.Computers in Biology and Medicine, 171:108151, 2024

Reference 38

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This paper cites SwinIR: Image restoration using swin transformer.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions SwinIR: Image restoration using swin transformer

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:32.585824Z

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This paper cites Enhanced deep residual networks for single image super-resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Enhanced deep residual networks for single image super-resolution

Reference 40

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9992–10002, 2021.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Swin transformer: Hierarchical vision transformer using shifted windows.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9992–10002, 2021

Reference 41

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This paper cites Swin transformer v2: Scaling up capacity and resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Swin transformer v2: Scaling up capacity and resolution

Reference 42

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This paper cites A novel 3d med- ical image super-resolution method based on densely con- nected network.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions A novel 3d med- ical image super-resolution method based on densely con- nected network

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:32.106606Z

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This paper cites The multimodal brain tumor image segmentation benchmark (brats).

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions The multimodal brain tumor image segmentation benchmark (brats)

Reference 44

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This paper cites Multi-input cardiac image super-resolution using convolu- tional neural networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Multi-input cardiac image super-resolution using convolu- tional neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:31.784753Z

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This paper cites Brain MRI super-resolution using deep 3d convolutional networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Brain MRI super-resolution using deep 3d convolutional networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:31.634760Z

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Observation 761a0a72-bdf7-4a62-b63e-bf137dcc87a0 · outbound

This paper cites Multiscale brain mri super-resolution using deep 3d convolutional networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Multiscale brain mri super-resolution using deep 3d convolutional networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:31.484745Z

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Observation 58bd9e81-e531-4a53-bfde-87e4cb7d82f4 · outbound

This paper cites Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity

Reference 48

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This paper cites Brain MRI super-resolution using 3D generative adversarial networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Brain MRI super-resolution using 3D generative adversarial networks

Reference 49

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Observation 1b98320c-4aae-4982-9e2c-72547094e5a5 · outbound

This paper cites Object Detection with Transformers: A Review.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Object Detection with Transformers: A Review

Reference 50

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This paper cites Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang

Reference 51

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This paper cites Deep robust residual network for super-resolution of 2d fetal brain mri.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Deep robust residual network for super-resolution of 2d fetal brain mri

Reference 52

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This paper cites Axiomatic attribution for deep networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Axiomatic attribution for deep networks

Reference 53

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MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Image super-resolution using dense skip connections

Reference 54

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Observation 1e90f7d9-4168-4758-a066-bb1290e3ddda · outbound

This paper cites The wu-minn human connec- tome project: an overview.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions The wu-minn human connec- tome project: an overview

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:30.904747Z

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This paper cites Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:30.774836Z

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This paper cites Imaging intact human organs with local resolu- tion of cellular structures using hierarchical phase-contrast tomography.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Imaging intact human organs with local resolu- tion of cellular structures using hierarchical phase-contrast tomography

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:30.636395Z

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This paper cites 3d dense convolutional neural network for fast and accurate single mr image super- resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions 3d dense convolutional neural network for fast and accurate single mr image super- resolution

Reference 58

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This paper cites Adjacent slices feature transformer net- work for single anisotropic 3d brain mri image super- resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Adjacent slices feature transformer net- work for single anisotropic 3d brain mri image super- resolution

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:30.460038Z

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MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Accelerat- ing magnetic resonance imaging via deep learning

Reference 60

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This paper cites Transbts: Multimodal brain tumor segmen- tation using transformer.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Transbts: Multimodal brain tumor segmen- tation using transformer

Reference 61

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Observation 9bd7bd73-35e5-410e-b721-61b4d38154cd · outbound

This paper cites ESRGAN: Enhanced super-resolution generative adversarial networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions ESRGAN: Enhanced super-resolution generative adversarial networks

Reference 62

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Observation a1d52896-4e36-4a5e-85f9-165c4a781de8 · outbound

This paper cites Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for transla- tional research from mouse to man.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for transla- tional research from mouse to man

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:29.954753Z

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Observation f2f24c05-c8ea-4e7a-a5d7-fb8a7b93d538 · outbound

This paper cites An arbitrary scale super- resolution approach for 3d mr images via implicit neural rep- resentation.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions An arbitrary scale super- resolution approach for 3d mr images via implicit neural rep- resentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:29.835392Z

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This paper cites Brain mr image super-resolution via a deep convolutional neural network with multi-unit up- sampling learning.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Brain mr image super-resolution via a deep convolutional neural network with multi-unit up- sampling learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:29.654746Z

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This paper cites 3d cross-scale feature transformer network for brain mr image super-resolution.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions 3d cross-scale feature transformer network for brain mr image super-resolution

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:29.484746Z

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Observation 70f9061d-d050-43bb-b520-bd7c8360c8f9 · outbound

This paper cites Image super-resolution using very deep residual channel attention networks.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Image super-resolution using very deep residual channel attention networks

Reference 67

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This paper cites Arbitrary scale super-resolution for medical images.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Arbitrary scale super-resolution for medical images

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:29.347197Z

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Observation 5ece7bf9-7d13-4ab8-81e2-2da45d3d296a · outbound

This paper cites 6 provides an overview of our proposed shifting vol- umetric hierarchical attention transformer (SVHAT) layer.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions 6 provides an overview of our proposed shifting vol- umetric hierarchical attention transformer (SVHAT) layer

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:30:29.284747Z

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Observation e0df5849-4dc5-4785-8b73-5b66a5128ddf · outbound

This paper cites We use the T1-weighted images which feature an isotropic resolution of 0.7 mm and a vol- ume size of 320×320×256 voxels.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions We use the T1-weighted images which feature an isotropic resolution of 0.7 mm and a vol- ume size of 320×320×256 voxels

Reference 72

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Observation 2719229b-0f98-469e-a262-88931a0b1fb3 · outbound

This paper cites To this end, we employ the LAM attribution method [26], which is a modification of the integrated gra- dient method [53] designed to investigate SR architectures.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions To this end, we employ the LAM attribution method [26], which is a modification of the integrated gra- dient method [53] designed to investigate SR architectures

Reference 73

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Observation 272a7242-9b24-46bb-a51a-b43502297814 · outbound

This paper cites 8 shows more visual comparisons of SR predic- tions using the datasets HCP 1200, IXI, BraTS 2023, Kirby 21, FACTS-Synth and FACTS-Real at ×4 upscal- ing.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions 8 shows more visual comparisons of SR predic- tions using the datasets HCP 1200, IXI, BraTS 2023, Kirby 21, FACTS-Synth and FACTS-Real at ×4 upscal- ing

Reference 74

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Observation c507c22c-f881-488f-a8f3-e4a7a1c081a1 · outbound

This paper cites 3 MTVNet: Mapping using Transformers for V olumes – Network for Super-Resolution with Long-Range Interactions Supplementary Material.

MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions 3 MTVNet: Mapping using Transformers for V olumes – Network for Super-Resolution with Long-Range Interactions Supplementary Material

Reference 2021

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Observation a6856cab-aeec-4bf5-9b51-6398f438cbd3 · outbound

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MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions Unresolved cited work

Reference 2022

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