在过去的几年中，卷积神经网络（CNN），尤其是U-NET，一直是医学图像处理时代的流行技术。具体而言，开创性的U-NET及其替代方案成功地设法解决了各种各样的医学图像分割任务。但是，这些体系结构在本质上是不完美的，因为它们无法表现出长距离相互作用和空间依赖性，从而导致具有可变形状和结构的医学图像分割的严重性能下降。针对序列到序列预测的初步提议的变压器已成为替代体系结构，以精确地模拟由自我激进机制辅助的全局信息。尽管设计了可行的设计，但利用纯变压器来进行图像分割目的，可能导致限制的定位容量，导致低级功能不足。因此，一系列研究旨在设计基于变压器的U-NET的强大变体。在本文中，我们提出了Trans-Norm，这是一种新型的深层分割框架，它随同将变压器模块合并为标准U-NET的编码器和跳过连接。我们认为，跳过连接的方便设计对于准确的分割至关重要，因为它可以帮助扩展路径和收缩路径之间的功能融合。在这方面，我们从变压器模块中得出了一种空间归一化机制，以适应性地重新校准跳过连接路径。对医学图像分割的三个典型任务进行了广泛的实验，证明了透气的有效性。代码和训练有素的模型可在https://github.com/rezazad68/transnorm上公开获得。

多年来，卷积神经网络（CNN）已成为多种计算机视觉任务的事实上的标准。尤其是，基于开创性体系结构（例如具有跳过连接的U形模型）或具有金字塔池的Artous卷积的深度神经网络已针对广泛的医学图像分析任务量身定制。此类架构的主要优点是它们容易拘留多功能本地功能。然而，作为一般共识，CNN无法捕获由于卷积操作的固有性能的内在特性而捕获长期依赖性和空间相关性。另外，从全球信息建模中获利的变压器源于自我发项机制，最近在自然语言处理和计算机视觉方面取得了出色的表现。然而，以前的研究证明，局部和全局特征对于密集预测的深层模型至关重要，例如以不同的形状和配置对复杂的结构进行分割。为此，本文提出了TransDeeplab，这是一种新型的DeepLab样纯变压器，用于医学图像分割。具体而言，我们用移动的窗口利用层次旋转式变形器来扩展DeepLabV3并建模非常有用的空间金字塔池（ASPP）模块。对相关文献的彻底搜索结果是，我们是第一个用基于纯变压器模型对开创性DeepLab模型进行建模的人。关于各种医学图像分割任务的广泛实验证明，我们的方法在视觉变压器和基于CNN的方法的合并中表现出色或与大多数当代作品相提并论，并显着降低了模型复杂性。代码和训练有素的模型可在https://github.com/rezazad68/transdeeplab上公开获得

卷积神经网络（CNN）已成为医疗图像分割任务的共识。但是，由于卷积操作的性质，它们在建模长期依赖性和空间相关性时受到限制。尽管最初开发了变压器来解决这个问题，但它们未能捕获低级功能。相比之下，证明本地和全球特征对于密集的预测至关重要，例如在具有挑战性的环境中细分。在本文中，我们提出了一种新型方法，该方法有效地桥接了CNN和用于医学图像分割的变压器。具体而言，我们使用开创性SWIN变压器模块和一个基于CNN的编码器设计两个多尺度特征表示。为了确保从上述两个表示获得的全局和局部特征的精细融合，我们建议在编码器编码器结构的跳过连接中提出一个双层融合（DLF）模块。在各种医学图像分割数据集上进行的广泛实验证明了Hiformer在计算复杂性以及定量和定性结果方面对其他基于CNN的，基于变压器和混合方法的有效性。我们的代码可在以下网址公开获取：https：//github.com/amirhossein-kz/hiformer

对医学图像的器官或病变的准确分割对于可靠的疾病和器官形态计量学的可靠诊断至关重要。近年来，卷积编码器解码器解决方案在自动医疗图像分割领域取得了重大进展。由于卷积操作中的固有偏见，先前的模型主要集中在相邻像素形成的局部视觉提示上，但无法完全对远程上下文依赖性进行建模。在本文中，我们提出了一个新型的基于变压器的注意力指导网络，称为Transattunet，其中多层引导注意力和多尺度跳过连接旨在共同增强语义分割体系结构的性能。受到变压器的启发，具有变压器自我注意力（TSA）和全球空间注意力（GSA）的自我意识注意（SAA）被纳入Transattunet中，以有效地学习编码器特征之间的非本地相互作用。此外，我们还使用解码器块之间的其他多尺度跳过连接来汇总具有不同语义尺度的上采样功能。这样，多尺度上下文信息的表示能力就可以增强以产生判别特征。从这些互补组件中受益，拟议的Transattunet可以有效地减轻卷积层堆叠和连续采样操作引起的细节损失，最终提高医学图像的细分质量。来自不同成像方式的多个医疗图像分割数据集进行了广泛的实验表明，所提出的方法始终优于最先进的基线。我们的代码和预培训模型可在以下网址找到：https：//github.com/yishuliu/transattunet。

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights. The code is publicly available at https://github.com/mindflow-institue/DAEFormer.

计算机辅助医学图像分割已广泛应用于诊断和治疗，以获得靶器官和组织的形状和体积的临床有用信息。在过去的几年中，基于卷积神经网络（CNN）的方法（例如，U-Net）占主导地位，但仍遭受了不足的远程信息捕获。因此，最近的工作提出了用于医学图像分割任务的计算机视觉变压器变体，并获得了有希望的表现。这种变压器通过计算配对贴片关系来模拟远程依赖性。然而，它们促进了禁止的计算成本，尤其是在3D医学图像（例如，CT和MRI）上。在本文中，我们提出了一种称为扩张变压器的新方法，该方法在本地和全球范围内交替捕获的配对贴片关系进行自我关注。灵感来自扩张卷积核，我们以扩张的方式进行全球自我关注，扩大接收领域而不增加所涉及的斑块，从而降低计算成本。基于这种扩展变压器的设计，我们构造了一个用于3D医学图像分割的U形编码器解码器分层体系结构。 Synapse和ACDC数据集的实验表明，我们的D-Ager Model从头开始培训，以低计算成本从划痕训练，优于各种竞争力的CNN或基于变压器的分段模型，而不耗时的每训练过程。

卷积神经网络（CNN）的深度学习体系结构在计算机视野领域取得了杰出的成功。 CNN构建的编码器架构U-Net在生物医学图像分割方面取得了重大突破，并且已在各种实用的情况下应用。但是，编码器部分中每个下采样层和简单堆积的卷积的平等设计不允许U-NET从不同深度提取足够的特征信息。医学图像的复杂性日益增加为现有方法带来了新的挑战。在本文中，我们提出了一个更深层，更紧凑的分裂注意U形网络（DCSAU-NET），该网络有效地利用了基于两个新颖框架的低级和高级语义信息：主要功能保护和紧凑的分裂注意力堵塞。我们评估了CVC-ClinicDB，2018 Data Science Bowl，ISIC-2018和SEGPC-2021数据集的建议模型。结果，DCSAU-NET在联合（MIOU）和F1-SOCRE的平均交点方面显示出比其他最先进的方法（SOTA）方法更好的性能。更重要的是，提出的模型在具有挑战性的图像上表现出了出色的细分性能。我们的工作代码以及更多技术细节，请访问https://github.com/xq141839/dcsau-net。

Recently, many attempts have been made to construct a transformer base U-shaped architecture, and new methods have been proposed that outperformed CNN-based rivals. However, serious problems such as blockiness and cropped edges in predicted masks remain because of transformers' patch partitioning operations. In this work, we propose a new U-shaped architecture for medical image segmentation with the help of the newly introduced focal modulation mechanism. The proposed architecture has asymmetric depths for the encoder and decoder. Due to the ability of the focal module to aggregate local and global features, our model could simultaneously benefit the wide receptive field of transformers and local viewing of CNNs. This helps the proposed method balance the local and global feature usage to outperform one of the most powerful transformer-based U-shaped models called Swin-UNet. We achieved a 1.68% higher DICE score and a 0.89 better HD metric on the Synapse dataset. Also, with extremely limited data, we had a 4.25% higher DICE score on the NeoPolyp dataset. Our implementations are available at: https://github.com/givkashi/Focal-UNet

Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module is used to aggregate the boundary characteristic from low-level features and semantic information from high-level features for better preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images, and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations such as small object segmentation and ambiguous object boundaries.

Transformers have made remarkable progress towards modeling long-range dependencies within the medical image analysis domain. However, current transformer-based models suffer from several disadvantages: (1) existing methods fail to capture the important features of the images due to the naive tokenization scheme; (2) the models suffer from information loss because they only consider single-scale feature representations; and (3) the segmentation label maps generated by the models are not accurate enough without considering rich semantic contexts and anatomical textures. In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation. First, we take advantage of the pyramid structure to construct multi-scale representations and handle multi-scale variations. We then design a novel class-aware transformer module to better learn the discriminative regions of objects with semantic structures. Lastly, we utilize an adversarial training strategy that boosts segmentation accuracy and correspondingly allows a transformer-based discriminator to capture high-level semantically correlated contents and low-level anatomical features. Our experiments demonstrate that CASTformer dramatically outperforms previous state-of-the-art transformer-based approaches on three benchmarks, obtaining 2.54%-5.88% absolute improvements in Dice over previous models. Further qualitative experiments provide a more detailed picture of the model's inner workings, shed light on the challenges in improved transparency, and demonstrate that transfer learning can greatly improve performance and reduce the size of medical image datasets in training, making CASTformer a strong starting point for downstream medical image analysis tasks.

Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global and local features and contextual representations which can be utilized for semantic output prediction by the decoder. Despite their success, the locality of convolutional layers in FCNNs, limits the capability of learning long-range spatial dependencies. Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem. We introduce a novel architecture, dubbed as UNEt TRansformers (UNETR), that utilizes a transformer as the encoder to learn sequence representations of the input volume and effectively capture the global multi-scale information, while also following the successful "U-shaped" network design for the encoder and decoder. The transformer encoder is directly connected to a decoder via skip connections at different resolutions to compute the final semantic segmentation output. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for multiorgan segmentation and the Medical Segmentation Decathlon (MSD) dataset for brain tumor and spleen segmentation tasks. Our benchmarks demonstrate new state-of-the-art performance on the BTCV leaderboard. Code: https://monai.io/research/unetr

变形金刚占据了自然语言处理领域，最近影响了计算机视觉区域。在医学图像分析领域中，变压器也已成功应用于全栈临床应用，包括图像合成/重建，注册，分割，检测和诊断。我们的论文旨在促进变压器在医学图像分析领域的认识和应用。具体而言，我们首先概述了内置在变压器和其他基本组件中的注意机制的核心概念。其次，我们回顾了针对医疗图像应用程序量身定制的各种变压器体系结构，并讨论其局限性。在这篇综述中，我们调查了围绕在不同学习范式中使用变压器，提高模型效率及其与其他技术的耦合的关键挑战。我们希望这篇评论可以为读者提供医学图像分析领域的读者的全面图片。

最新的语义分段方法采用具有编码器解码器架构的U-Net框架。 U-Net仍然具有挑战性，具有简单的跳过连接方案来模拟全局多尺度上下文：1）由于编码器和解码器级的不兼容功能集的问题，并非每个跳过连接设置都是有效的，甚至一些跳过连接对分割性能产生负面影响; 2）原始U-Net比某些数据集上没有任何跳过连接的U-Net更糟糕。根据我们的调查结果，我们提出了一个名为Uctransnet的新分段框架（在U-Net中的提议CTRANS模块），从引导机制的频道视角。具体地，CTRANS模块是U-NET SKIP连接的替代，其包括与变压器（命名CCT）和子模块通道 - 明智的跨关注进行多尺度信道交叉融合的子模块（命名为CCA）以指导熔融的多尺度通道 - 明智信息，以有效地连接到解码器功能以消除歧义。因此，由CCT和CCA组成的所提出的连接能够替换原始跳过连接以解决精确的自动医学图像分割的语义间隙。实验结果表明，我们的UCTRANSNET产生更精确的分割性能，并通过涉及变压器或U形框架的不同数据集和传统架构的语义分割来实现一致的改进。代码：https：//github.com/mcgregorwwwww/uctransnet。

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters and compute cost. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient having linear complexity with respect to the input sequence length. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the overall network parameters. Our extensive evaluations on three benchmarks, Synapse, BTCV and ACDC, reveal the effectiveness of the proposed contributions in terms of both efficiency and accuracy. On Synapse dataset, our UNETR++ sets a new state-of-the-art with a Dice Similarity Score of 87.2%, while being significantly efficient with a reduction of over 71% in terms of both parameters and FLOPs, compared to the best existing method in the literature. Code: https://github.com/Amshaker/unetr_plus_plus.

基于CNN的方法已经实现了医学图像分割的令人印象深刻的结果，但由于卷积操作的内在局部，它们未能捕获远程依赖性。基于变压器的方法最近在愿景任务中流行，因为它们的远程依赖性和有希望的性能。但是，它缺乏建模本地背景。本文以医学图像分割为例，我们呈现了MissFormer，一种有效和强大的医学图像分割变压器。 MissFormer是具有两个吸引人设计的分层编码器 - 解码器网络：1）通过所提出的增强型变压器块重新设计前馈网络，该熵增强了远程依赖性并补充本地上下文，使得该特征更加辨别。 2）我们提出了增强的变压器上下文网桥，与以前的模拟全局信息的方法不同，所提出的上下文网桥与增强变压器块提取了由我们的层级变压器编码器产生的多尺度特征的远程依赖性和本地语境。由这两个设计驱动，MissFormer显示了捕获更多辨别性依赖性和在医学图像分割中的识别依赖性和上下文的牢固能力。多器官和心脏分割任务的实验表明了我们的错过更优越性，有效性和稳健性，训练了从划伤的痕迹甚至高于想象的最先进方法。核心设计可以推广到其他视觉分段任务。代码已在GitHub上发布：https://github.com/zhifangdeng/missformer

表面缺陷检测是确保工业产品质量的极其至关重要的步骤。如今，基于编码器架构的卷积神经网络（CNN）在各种缺陷检测任务中取得了巨大的成功。然而，由于卷积的内在局部性，它们通常在明确建模长距离相互作用时表现出限制，这对于复杂情况下的像素缺陷检测至关重要，例如杂乱的背景和难以辨认的伪缺陷。最近的变压器尤其擅长学习全球图像依赖性，但对于详细的缺陷位置所需的本地结构信息有限。为了克服上述局限性，我们提出了一个有效的混合变压器体系结构，称为缺陷变压器（faft），用于表面缺陷检测，该检测将CNN和Transferaler纳入统一模型，以协作捕获本地和非本地关系。具体而言，在编码器模块中，首先采用卷积茎块来保留更详细的空间信息。然后，贴片聚合块用于生成具有四个层次结构的多尺度表示形式，每个层次结构之后分别是一系列的feft块，该块分别包括用于本地位置编码的本地位置块，一个轻巧的多功能自我自我 - 注意与良好的计算效率建模多尺度的全球上下文关系，以及用于功能转换和进一步位置信息学习的卷积馈送网络。最后，提出了一个简单但有效的解码器模块，以从编码器中的跳过连接中逐渐恢复空间细节。与其他基于CNN的网络相比，三个数据集上的广泛实验证明了我们方法的优势和效率。

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realize global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissues structures. Inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting anatomies of 133 structures in brain, 14 organs in abdomen, 4 hierarchical components in kidney, and inter-connected kidney tumors). We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in single network, outperforms prior state-of-the-art method SLANT27 ensembled with 27 network tiles, our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively.

作为新一代神经体系结构的变形金刚在自然语言处理和计算机视觉方面表现出色。但是，现有的视觉变形金刚努力使用有限的医学数据学习，并且无法概括各种医学图像任务。为了应对这些挑战，我们将Medformer作为数据量表变压器呈现为可推广的医学图像分割。关键设计结合了理想的电感偏差，线性复杂性的层次建模以及以空间和语义全局方式以线性复杂性的关注以及多尺度特征融合。 Medformer可以在不预训练的情况下学习微小至大规模的数据。广泛的实验表明，Medformer作为一般分割主链的潜力，在三个具有多种模式（例如CT和MRI）和多样化的医学靶标（例如，健康器官，疾病，疾病组织和肿瘤）的三个公共数据集上优于CNN和视觉变压器。我们将模型和评估管道公开可用，为促进广泛的下游临床应用提供固体基线和无偏比较。

识别息肉对于在计算机辅助临床支持系统中自动分析内窥镜图像的自动分析具有挑战性。已经提出了基于卷积网络（CNN），变压器及其组合的模型，以分割息肉以有希望的结果。但是，这些方法在模拟息肉的局部外观方面存在局限性，或者在解码过程中缺乏用于空间依赖性的多层次特征。本文提出了一个新颖的网络，即结肠形式，以解决这些局限性。 Colonformer是一种编码器架构，能够在编码器和解码器分支上对远程语义信息进行建模。编码器是一种基于变压器的轻量级体系结构，用于在多尺度上建模全局语义关系。解码器是一种层次结构结构，旨在学习多层功能以丰富特征表示。此外，添加了一个新的Skip连接技术，以完善整体地图中的息肉对象的边界以进行精确分割。已经在五个流行的基准数据集上进行了广泛的实验，以进行息肉分割，包括Kvasir，CVC-Clinic DB，CVC-ColondB，CVC-T和Etis-Larib。实验结果表明，我们的结肠构造者在所有基准数据集上的表现优于其他最先进的方法。

卷积神经网络（CNN），是基于深度学习的医学图像分析的最普遍的体系结构，在功能上仍受其固有的电感偏见和不充分的接收场的限制。旨在解决这个问题的变压器由于其出色的捕获长期依赖的能力而引起了自然语言处理和计算机视觉的爆炸性关注。但是，最新的基于变压器的医学图像分割方法直接将香草变压器作为基于CNN的方法中的辅助模块应用于辅助模块，从而导致由于变压器中刚性贴片分配方案而导致严重的细节损失。为了解决这个问题，我们提出了C2FTRANS，这是一种新型的多尺度架构，将医学图像分割作为粗到精细的过程。 C2FTRAN主要由跨尺度的全局变压器（CGT）组成，该变压器（CGT）解决了CNN中的局部上下文相似性和边界感知的局部变压器（BLT），该局部变压器（BLT）克服了通过变压器中的刚性贴片分配带来的边界不确定性。具体而言，CGT在三个不同的小规模特征图上建立全球依赖性，以获得具有可接受的计算成本的丰富全球语义特征，而BLT通过在熵的指导下适应围绕边界的窗口来捕获中端依赖性，以降低计算复杂性并最小化最小基于大规模特征地图的详细损失。三个公共数据集的广泛实验结果证明了C2FTRAN的卓越性能与基于CNN的最新基于CNN和基于变压器的方法具有更少的参数和较低的拖失术。我们认为，C2Ftrans的设计将进一步激发未来在开发高效和轻量级变压器以进行医学图像细分方面的工作。本文的源代码可在https://github.com/xianlin7/c2ftrans上公开获得。