A transformer-based generative adversarial network for brain tumor segmentation

Huang, Liqun; Zhu, Enjun; Chen, Long; Wang, Zhaoyang; Chai, Senchun; Zhang, Baihai

doi:10.3389/fnins.2022.1054948

Cited by 17 publications

(6 citation statements)

References 57 publications

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“…This framework integrated a generator utilizing a 3D CNN-based encoder and decoder architecture, alongside a Resnet module and transformer block. Incorporating deep supervision and hierarchical feature analysis, this method showcased superior generalization for brain tumor segmentation compared to other approaches [9].…”

Section: Literature Reviewmentioning

confidence: 98%

Role of Dual Energy Computed Tomography in Inflammatory Bowel Disease

Singh

Rai

Mroueh

et al. 2022

Seminars in Ultrasound, CT and MRI

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Section: Literature Reviewmentioning

confidence: 98%

Role of Dual Energy Computed Tomography in Inflammatory Bowel Disease

Singh

Rai

Mroueh

et al. 2022

Seminars in Ultrasound, CT and MRI

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“…Transformer-based methods for glioma included. [49][50][51][52]55,57,59 Jiang et al 59 proposed a SwinBTS to introduce the SwinTransformer to a U-shape structure to fulfill the task of 3D brain tumor…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

“…Accounting for 80% of malignant brain tumors, glioma is difficult to automatically diagnosed due to its changeable appearance and ambiguous boundary. Transformer‐based methods for glioma included 49–52,55,57,59 . Jiang et al 59 proposed a SwinBTS to introduce the SwinTransformer to a U‐shape structure to fulfill the task of 3D brain tumor segmentation.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

Recent advances of Transformers in medical image analysis: A comprehensive review

Xia

Wang

2023

MedComm – Future Medicine

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Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario-specific architectural variations.In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer-based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up-to-date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.

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“…The Transformer structure is proposed based only on the self-attention mechanism, completely eliminating the convolutional structure, and is powerful in modeling global context is powerful, and several studies have shown that Transformer-based frameworks also achieve state-of-the-art performance on a variety of computer vision tasks ( 21 ). However, the self-attentiveness in Transformer requires large computation and memory consumption when dealing with long sequences, and the sparse nature of medical image data makes the model prone to overfitting during the training session, which hinders the application of Transformer in medical image segmentation tasks, which has been tried and tested in the field of natural image processing ( 22 , 23 ). To reduce the number of computational parameters, we refer to the approach in Swin Transformer, which uses two layers of attention structures with a hierarchical design, including a non-overlapping local window, and an overlapping cross-window ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules

Ma,

Yuan,

Guo

et al. 2023

Front. Med.

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Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we investigate the segmentation of lung nodule images. We address the above-mentioned problems of medical image segmentation algorithms and conduct research on medical image fusion algorithms based on a hybrid channel-space attention mechanism and medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks (CNN) and Visual Transformer. To the problem that medical image segmentation algorithms are difficult to capture long-range feature dependencies, this paper proposes a medical image segmentation model SW-UNet based on a hybrid CNN and Vision Transformer (ViT) framework. Self-attention mechanism and sliding window design of Visual Transformer are used to capture global feature associations and break the perceptual field limitation of convolutional operations due to inductive bias. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on the LUNA16 lung nodule image dataset validate the algorithm and show that the proposed network can achieve efficient medical image segmentation on a lightweight scale. In addition, to validate the migratability of the model, we performed additional validation on other tumor datasets with desirable results. Our research addresses the crucial need for improved medical image segmentation algorithms. By introducing the SW-UNet model, which combines CNN and ViT, we successfully capture long-range feature dependencies and break the perceptual field limitations of traditional convolutional operations. This approach not only enhances the efficiency of medical image segmentation but also maintains model scalability and adaptability to small medical datasets. The positive outcomes on various tumor datasets emphasize the potential migratability and broad applicability of our proposed model in the field of medical image analysis.

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A transformer-based generative adversarial network for brain tumor segmentation

Cited by 17 publications

References 57 publications

Role of Dual Energy Computed Tomography in Inflammatory Bowel Disease

Role of Dual Energy Computed Tomography in Inflammatory Bowel Disease

Recent advances of Transformers in medical image analysis: A comprehensive review

SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules

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