Eformer: Edge Enhancement based Transformer for Medical Image Denoising

Luthra, Achleshwar; Sulakhe, Harsh; Mittal, Tanish; Iyer, Abhishek M.; Yadav, Santosh Kumar

doi:10.48550/arxiv.2109.08044

Cited by 24 publications

(22 citation statements)

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“…Recently, Eformer used self-attention and depth-wise convolution for better local context capture in medical image denoising. 55 Our novel network MIST-net was developed to explore transformers in sparse-view data reconstruction. Again, we designed a Swin Recformer sub-network by combining the Swin transformer and convolution layer to make full use of both shallow and deep features.…”

Section: Methodsmentioning

confidence: 99%

Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction

Pan

Zhang

et al. 2022

Patterns

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Section: Methodsmentioning

confidence: 99%

Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction

Pan

Zhang

et al. 2022

Patterns

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“…TED-Net shows favorable performance on the Mayo Clinic LDCT dataset [286]. In another work, Luthra et al [291] propose Eformer which is Transformer-based residual learning architecture for LDCT images denoising.…”

Section: Ldct Enhancementmentioning

confidence: 99%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

Preprint

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Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

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“…Promising results have been reported employing Transformers in medical image denoising problems, such as lowdose CT denoising , Luthra et al, 2021 and low-count PET/MRI denoising . However, these studies fail to address the challenge of poor scaling to large input resolutions, and only work on small (64 × 64 − 128 × 128) images via either downsampling the original dataset [Luthra et al, 2021] or by slicing the large input images into smaller patches before passing them to the denoiser. In contrast, our proposed architecture works directly on the large resolution images that often arise in MRI reconstruction.…”

Section: Related Workmentioning

confidence: 99%

HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction

Fabian¹,

Soltanolkotabi²

2022

Preprint

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In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of undersampled and noisy measurements. Deep learning approaches have been proven to be successful in solving this ill-posed inverse problem and are capable of producing very high quality reconstructions. However, current architectures heavily rely on convolutions, that are content-independent and have difficulties modeling long-range dependencies in images. Recently, Transformers, the workhorse of contemporary natural language processing, have emerged as powerful building blocks for a multitude of vision tasks. These models split input images into non-overlapping patches, embed the patches into lower-dimensional tokens and utilize a self-attention mechanism that does not suffer from the aforementioned weaknesses of convolutional architectures. However, Transformers incur extremely high compute and memory cost when 1) the input image resolution is high and 2) when the image needs to be split into a large number of patches to preserve fine detail information, both of which are typical in low-level vision problems such as MRI reconstruction, having a compounding effect. To tackle these challenges, we propose HUMUS-Net, a hybrid architecture that combines the beneficial implicit bias and efficiency of convolutions with the power of Transformer blocks in an unrolled and multi-scale network. HUMUS-Net extracts high-resolution features via convolutional blocks and refines low-resolution features via a novel Transformer-based multi-scale feature extractor. Features from both levels are then synthesized into a high-resolution output reconstruction. Our network establishes new state of the art on the largest publicly available MRI dataset, the fastMRI dataset. We further demonstrate the performance of HUMUS-Net on two other popular MRI datasets and perform fine-grained ablation studies to validate our design. We note that the current experimental results are preliminary, we will update this paper soon with our final results involving larger models.

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Eformer: Edge Enhancement based Transformer for Medical Image Denoising

Cited by 24 publications

References 0 publications

Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction

Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction

Transformers in Medical Imaging: A Survey

HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction

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