SMESwin Unet: Merging CNN and Transformer for Medical Image Segmentation

Wang, Ziheng; Min, Xiongkuo; Shi, Fangyu; Jin, Ruinian; Nawrin, Saida S.; Yu, Ichen; Nagatomi, Ryoichi

doi:10.1007/978-3-031-16443-9_50

Cited by 8 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SMESwin Unet [51] Superpixel and MCCT-based channel-wise cross-fusion transformer (CCT) coupled with multi-scale semantic features and attention maps (Swin UNet).…”

Section: Cnn Design Description Specific Applicationmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

View full text Add to dashboard Cite

In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

show abstract

“…SMESwin Unet [51] Superpixel and MCCT-based channel-wise cross-fusion transformer (CCT) coupled with multi-scale semantic features and attention maps (Swin UNet).…”

Section: Cnn Design Description Specific Applicationmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

View full text Add to dashboard Cite

show abstract

“…Although many neural network models have been proposed for image segmentation in recent years, [32][33][34][35] we chose a modified version of the traditional U-Net for organ segmentation in our study. This decision was based on the fact that U-Net can easily achieve good results on small datasets, and the segmentation task in our study is relatively straightforward.…”

Section: Network Architecture For Organ Segmentationmentioning

confidence: 99%

A semi‐supervised learning‐based quality evaluation system for digital chest radiographs

Wei,

Qiu,

et al. 2023

Medical Physics

View full text Add to dashboard Cite

BackgroundDigital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential step in medical examinations. However, manual evaluation can be time‐consuming, labor‐intensive, and prone to interobserver differences, making it less reliable.PurposeTo alleviate the workload of radiographic technologists and enhance the efficiency of radiograph quality evaluation, it is crucial to develop rapid and reliable quality evaluation methods and establish a set of quantitative evaluation standards. To address this, we have proposed a quality evaluation system for digital radiographs that utilizes deep learning techniques to achieve fast and precise evaluation.MethodsThe evaluation of frontal chest radiograph quality involves assessing patient positioning through semantic segmentation and foreign body detection. For lung, scapula, and clavicle segmentation in digital chest radiographs, a residual connection‐based convolutional neural network π‐ResUNet, was proposed. Criteria for patient positioning evaluation were established based on the segmentation and manual evaluation results. A convolutional neural network, FasterRCNN, was utilized to detect and localize foreign bodies in digital chest radiographs. To enhance the performance of both neural networks, a semi‐supervised learning (SSL) strategy was implemented by incorporating a consistency loss that leverages a large number of unlabeled digital radiographs. We also trained the network using the fully supervised learning (FSL) strategy and compared their performance on the test set. The ChestXRay‐14 and object‐CXR datasets were used throughout the process.ResultsBy comparing with the manual annotation, the proposed network, trained using the SSL method, achieved a high Dice similarity coefficient (DSC) of 0.96, 0.88, and 0.88 for lung, scapula, and clavicle segmentation, respectively, outperforming the network trained with the FSL method. In addition, for foreign body detection, the proposed SSL method was superior to the FSL method, achieving an AUC (Area under receiver operating characteristic curve, Area under ROC curve) of 0.90 and an FROC (Free‐response ROC) of 0.77 on the test dataset.ConclusionsThe experimental results show that our proposed system is well‐suited for radiograph quality evaluation, with the semi‐supervised learning method further improving the network's performance. The proposed method can evaluate the quality of a chest radiograph from two aspects—patient positioning and foreign body detection—within 1 s, offering a promising tool in radiograph quality evaluation.

show abstract

ACC-UNet: A Completely Convolutional UNet Model for the 2020s

Ibtehaz,

Kihara

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

SMESwin Unet: Merging CNN and Transformer for Medical Image Segmentation

Cited by 8 publications

References 47 publications

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

A semi‐supervised learning‐based quality evaluation system for digital chest radiographs

ACC-UNet: A Completely Convolutional UNet Model for the 2020s

Contact Info

Product

Resources

About