Colorectal Polyp Segmentation Research Articles

Rethinking encoder-decoder architecture using vision transformer for colorectal polyp and surgical instruments segmentation

Accurate polyp segmentation from colonoscopy images is important for the immediate diagnosis and effective treatment of colon cancer. While significant progress has been made in the polyps segmentation task, there are various challenges that need to be addressed. Polyps can vary greatly in size and shape, and often has no clear boundary between surrounding tissues. Furthermore, surgical instrument segmentation can aid surgeons with the precise positioning and orientation of the instruments, helping them to plan the next steps in the robot-assisted surgery. The proposed colorectal polyp segmentation Transformer (CPS-Former) uses innovative attention blocks in a network that encodes-decodes features like classic Semantic Segmentation Network (SegNet). However, it has special self-attention modules with small convolutional kernels that efficiently extract information from different feature-channels. Moreover, it is equipped with an effective positional embedding to capture information from a large area of context for long distance interactions. Additionally, a fusion block is embedded for scaling-attention that combines the outputs from the encoder-decoder blocks to enhance the semantic features and reduce the non-semantic ones. Transformer encoder blocks also modified by adding a local feedforward layer and skips connections, and adjust the channel sizes to reduce the model trainable parameters. We evaluate our colorectal polyp segmentation network (CPS-Former) on four colorectal polyp public datasets and one surgical instrument segmentation dataset, which show its superiority over other state-of-the-art polyp segmentation models. Our implementation source code and network weights are available at GitHub: https://github.com/ahmedeqbal/CPS-Former.

Multi-scale and multi-path cascaded convolutional network for semantic segmentation of colorectal polyps

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation

Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully supervised, requiring extensive, precise, manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model—the Segment Anything Model (SAM)—into the field of medical image segmentation. Fine-tuning the foundation model is an effective approach to tackle sample scarcity. However, current SAM fine-tuning techniques still rely on precise annotations. To overcome this limitation, we propose WSPolyp-SAM, a novel weakly supervised approach for colonoscopy polyp segmentation. WSPolyp-SAM utilizes weak annotations to guide SAM in generating segmentation masks, which are then treated as pseudo-labels to guide the fine-tuning of SAM, thereby reducing the dependence on precise annotation data. To improve the reliability and accuracy of pseudo-labels, we have designed a series of enhancement strategies to improve the quality of pseudo-labels and mitigate the negative impact of low-quality pseudo-labels. Experimental results on five medical image datasets demonstrate that WSPolyp-SAM outperforms current fully supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Furthermore, by using different amounts of training data in weakly supervised and fully supervised experiments, it is found that weakly supervised fine-tuning can save 70% to 73% of annotation time costs compared to fully supervised fine-tuning. This study provides a new perspective on the combination of weakly supervised learning and SAM models, significantly reducing annotation time and offering insights for further development in the field of colonoscopy polyp segmentation.

Improving Polyp Segmentation with Boundary-Assisted Guidance and Cross-Scale Interaction Fusion Transformer Network

Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorectal polyps with complex shapes, they have two main shortcomings: (1) multi-level semantic information at the output of the encoder may result in information loss during the fusion process and (2) failure to adequately suppress background noise during segmentation. To address these challenges, we propose a cross-scale interaction fusion transformer for polyp segmentation (CIFFormer). Firstly, a novel feature supplement module (FSM) supplements the missing details and explores potential features to enhance the feature representations. Additionally, to mitigate the interference of background noise, we designed a cross-scale interactive fusion module (CIFM) that combines feature information between different layers to obtain more multi-scale and discriminative representative features. Furthermore, a boundary-assisted guidance module (BGM) is proposed to help the segmentation network obtain boundary-enhanced details. Extensive experiments on five typical datasets have demonstrated that CIFFormer has an obvious advantage in segmenting polyps. Specifically, CIFFormer achieved an mDice of 0.925 and an mIoU of 0.875 on the Kvasir-SEG dataset, achieving superior segmentation accuracy to competing methods.

Polyp segmentation network based on lightweight model and reverse attention mechanisms

AbstractColorectal cancer is a common gastrointestinal malignancy. Early screening and segmentation of colorectal polyps are of great clinical significance. Colonoscopy is the most effective method to detect polyps, but some polyps may be missed in the detection process. On this basis, the use of computer‐aided diagnosis technology is particularly important for colorectal polyp segmentation. To improve the detection rate of intestinal polyps under colonoscopy, a polyp segmentation network (MobileRaNet) based on a lightweight model and reverse attention (RA) mechanism was proposed to accurately segment polyps in colonoscopy images. The coordinated attention module is used to improve MobileNetV3 and make it the backbone network (CaNet). Second, a part of the output of the high‐level feature from the backbone network is passed into the parallel axial receptive field module (PA_RFB) to extract the global dependency representation without losing the details. Third, a global map is generated based on this combined feature as the initial boot area of the subsequent components. Finally, the RA module is used to mine the target region and boundary clues to improve the segmentation accuracy. To verify the effectiveness and lightweight performance of the algorithm, five challenging datasets, including CVC‐ColonDB, CVC‐300, and Kvasir, are used in this paper. In six indexes, including MeanDice, MeanIoU, and MAE, compared with seven typical models such as PraNet and TransUnet, accuracy, FLOPs, parameters, and FPS were compared. The experimental results show that the MobileRaNet proposed in this paper has improved the performance of the five datasets to varying degrees, especially the MeanDice and MeanIOU indexes of the Kvasir dataset reach 91.2% and 85.6%, which are, respectively, increased by 1.4% and 1.6% compared with PraNet. Compared with PraNet, FLOPs and parameters decreased by 83.3% and 76.7%, respectively.

UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images.

Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is pivotal for timely detection and removal, contributing significantly to CRC prevention. The manual analysis of images generated by gastrointestinal screening technologies poses a tedious task for doctors. Therefore, computer vision-assisted cancer detection could serve as an efficient tool for polyp segmentation. Numerous efforts have been dedicated to automating polyp localization, with the majority of studies relying on convolutional neural networks (CNNs) to learn features from polyp images. Despite their success in polyp segmentation tasks, CNNs exhibit significant limitations in precisely determining polyp location and shape due to their sole reliance on learning local features from images. While gastrointestinal images manifest significant variation in their features, encompassing both high- and low-level ones, a framework that combines the ability to learn both features of polyps is desired. This paper introduces UViT-Seg, a framework designed for polyp segmentation in gastrointestinal images. Operating on an encoder-decoder architecture, UViT-Seg employs two distinct feature extraction methods. A vision transformer in the encoder section captures long-range semantic information, while a CNN module, integrating squeeze-excitation and dual attention mechanisms, captures low-level features, focusing on critical image regions. Experimental evaluations conducted on five public datasets, including CVC clinic, ColonDB, Kvasir-SEG, ETIS LaribDB, and Kvasir Capsule-SEG, demonstrate UViT-Seg's effectiveness in polyp localization. To confirm its generalization performance, the model is tested on datasets not used in training. Benchmarking against common segmentation methods and state-of-the-art polyp segmentation approaches, the proposed model yields promising results. For instance, it achieves a mean Dice coefficient of 0.915 and a mean intersection over union of 0.902 on the CVC Colon dataset. Furthermore, UViT-Seg has the advantage of being efficient, requiring fewer computational resources for both training and testing. This feature positions it as an optimal choice for real-world deployment scenarios.

Multi-scale nested UNet with transformer for colorectal polyp segmentation.

Polyp detection and localization are essential tasks for colonoscopy. U-shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long-range dependencies modeling limits their receptivefields. Our goal was to develop and test a novel architecture for polyp segmentation, which takes advantage of learning local information with long-range dependenciesmodeling. A novel architecture combining with multi-scale nested UNet structure integrated transformer for polyp segmentation was developed. The proposed network takes advantage of both CNN and transformer to extract distinct feature information. The transformer layer is embedded between the encoder and decoder of a U-shape net to learn explicit global context and long-range semantic information. To address the challenging of variant polyp sizes, a MSFF unit was proposed to fuse features with multipleresolution. Four public datasets and one in-house dataset were used to train and test the model performance. Ablation study was also conducted to verify each component of the model. For dataset Kvasir-SEG and CVC-ClinicDB, the proposed model achieved mean dice score of 0.942 and 0.950 respectively, which were more accurate than the other methods. To show the generalization of different methods, we processed two cross dataset validations, the proposed model achieved the highest mean dice score. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-artmethods. The proposed model produced more accurate polyp segmentation than current methods on four different public and one in-house datasets. Its capability of polyps segmentation in different sizes shows the potential clinicalapplication.

EfficientPolypSeg: Efficient Polyp Segmentation in colonoscopy images using EfficientNet-B5 with dilated blocks and attention mechanisms

Background:Colorectal cancer ranks as the second leading cause of cancer-related death worldwide, prompting the need for effective strategies to combat this disease. The urgent need for effective strategies to combat this disease has led to the exploration of innovative approaches in medical imaging and diagnosis. Automatic segmentation and detection of colorectal polyps in colonoscopy images have emerged as a promising approach, enabling timely identification and diagnosis of colorectal diseases. Methods:This paper introduces EfficientPolypSeg, an advanced segmentation architecture that leverages the capabilities of EfficientNet-B5 as the encoder, enhanced by two dilated blocks, a Channel Attention Block (CAB), a Spatial Attention Block (SAB), and a Feature Fusion Block (FFB). Notably, our model exhibits superior performance compared to state-of-the-art methods, achieving exceptional accuracy and segmentation quality. Results:To validate the effectiveness of EfficientPolypSeg, extensive experiments were conducted on four publicly available datasets. The results demonstrate the remarkable performance of our model, showcasing its ability to outperform existing approaches in terms of accuracy and segmentation quality. Our model achieved an impressive mean Dice score of 0.92 and a mean Intersection over Union (IoU) of 0.86 on these datasets. These findings highlight the potential impact of EfficientPolypSeg in improving the detection and diagnosis of colorectal polyps. Conclusion:EfficientPolypSeg, the segmentation architecture proposed in this study, exhibits remarkable capabilities in the automatic detection and segmentation of colorectal polyps. The model’s superior performance, as evidenced by extensive experimentation on diverse datasets, emphasizes its potential impact on improving the detection and diagnosis of colorectal diseases.

Unveiling camouflaged and partially occluded colorectal polyps: Introducing CPSNet for accurate colon polyp segmentation

Background:Segmenting colorectal polyps presents a significant challenge due to the diverse variations in their size, shape, texture, and intricate backgrounds. Particularly demanding are the so-called “camouflaged” polyps, which are partially concealed by surrounding tissues or fluids, adding complexity to their detection. Methods:We present CPSNet, an innovative model designed for camouflaged polyp segmentation. CPSNet incorporates three key modules: the Deep Multi-Scale-Feature Fusion Module, the Camouflaged Object Detection Module, and the Multi-Scale Feature Enhancement Module. These modules work collaboratively to improve the segmentation process, enhancing both robustness and accuracy. Results:Our experiments confirm the effectiveness of CPSNet. When compared to state-of-the-art methods in colon polyp segmentation, CPSNet consistently outperforms the competition. Particularly noteworthy is its performance on the ETIS-LaribPolypDB dataset, where CPSNet achieved a remarkable 2.3% increase in the Dice coefficient compared to the Polyp-PVT model. Conclusion:In summary, CPSNet marks a significant advancement in the field of colorectal polyp segmentation. Its innovative approach, encompassing multi-scale feature fusion, camouflaged object detection, and feature enhancement, holds considerable promise for clinical applications.

AFANet: Adaptive feature aggregation for polyp segmentation

In terms of speed and accuracy, the deep learning-based polyp segmentation method is superior. It is essential for the early detection and treatment of colorectal cancer and has the potential to greatly reduce the disease's overall prevalence. Due to the various forms and sizes of polyps, as well as the blurring of the boundaries between the polyp region and the surrounding mucus, most existing algorithms are unable to provide highly accurate colorectal polyp segmentation. Therefore, to overcome these obstacles, we propose an adaptive feature aggregation network (AFANet). It contains two main modules: the Multi-modal Balancing Attention Module (MMBA) and the Global Context Module (GCM). The MMBA extracts improved local characteristics for inference by integrating local contextual information while paying attention to them in three regions: foreground, background, and border. The GCM takes global information from the top of the encoder and sends it to the decoder layer in order to further investigate global contextual feature information in the pathologic picture. Dice of 92.11 % and 94.76 % and MIoU of 91.07 % and 94.54 %, respectively, are achieved by comprehensive experimental validation of our proposed technique on two benchmark datasets, Kvasir-SEG and CVCClinicDB. The experimental results demonstrate that the strategy outperforms other cutting-edge approaches.

Federated Semi-Supervised Medical Image Segmentation via Prototype-Based Pseudo-Labeling and Contrastive Learning.

Existing federated learning works mainly focus on the fully supervised training setting. In realistic scenarios, however, most clinical sites can only provide data without annotations due to the lack of resources or expertise. In this work, we are concerned with the practical yet challenging federated semi-supervised segmentation (FSSS), where labeled data are only with several clients and other clients can just provide unlabeled data. We take an early attempt to tackle this problem and propose a novel FSSS method with prototype-based pseudo-labeling and contrastive learning. First, we transmit a labeled-aggregated model, which is obtained based on prototype similarity, to each unlabeled client, to work together with the global model for debiased pseudo labels generation via a consistency- and entropy-aware selection strategy. Second, we transfer image-level prototypes from labeled datasets to unlabeled clients and conduct prototypical contrastive learning on unlabeled models to enhance their discriminative power. Finally, we perform the dynamic model aggregation with a designed consistency-aware aggregation strategy to dynamically adjust the aggregation weights of each local model. We evaluate our method on COVID-19 X-ray infected region segmentation, COVID-19 CT infected region segmentation and colorectal polyp segmentation, and experimental results consistently demonstrate the effectiveness of our proposed method. Codes areavailable at https://github.com/zhangbaiming/FedSemiSeg.

Rethinking Polyp Segmentation From An Out-of-distribution Perspective

Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach. We leverage the ability of masked autoencoders–self-supervised vision transformers trained on a reconstruction task–to learn in-distribution representations, here, the distribution of healthy colon images. We then perform out-of-distribution reconstruction and inference, with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy samples. We generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution (i.e., polyp) segmentation. Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets. Our code is publicly available at https://github.com/GewelsJI/Polyp-OOD.

PSTNet: Enhanced Polyp Segmentation With Multi-Scale Alignment and Frequency Domain Integration.

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC.

Progressive Feature Enhancement Network for Automated Colorectal Polyp Segmentation

Progressive Feature Enhancement Network for Automated Colorectal Polyp Segmentation

Boundary Refinement Network for Colorectal Polyp Segmentation in Colonoscopy Images

Boundary Refinement Network for Colorectal Polyp Segmentation in Colonoscopy Images

Boundary uncertainty aware network for automated polyp segmentation

Recently, leveraging deep neural networks for automated colorectal polyp segmentation has emerged as a hot topic due to the favored advantages in evading the limitations of visual inspection, e.g., overwork and subjectivity. However, most existing methods do not pay enough attention to the uncertain areas of colonoscopy images and often provide unsatisfactory segmentation performance. In this paper, we propose a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. Specifically, considering that polyps vary greatly in size and shape, we first adopt a pyramid vision transformer encoder to learn multi-scale feature representations. Then, a simple yet effective boundary exploration module (BEM) is proposed to explore boundary cues from the low-level features. To make the network focus on the ambiguous area where the prediction score is biased to neither the foreground nor the background, we further introduce a boundary uncertainty aware module (BUM) that explores error-prone regions from the high-level features with the assistance of boundary cues provided by the BEM. Through the top-down hybrid deep supervision, our BUNet implements coarse-to-fine polyp segmentation and finally localizes polyp regions precisely. Extensive experiments on five public datasets show that BUNet is superior to thirteen competing methods in terms of both effectiveness and generalization ability.

CSwinDoubleU-Net: A double U-shaped network combined with convolution and Swin Transformer for colorectal polyp segmentation

The early detection of polyps from colonoscopy can reduce the risk of colorectal cancer development and give timely treatment. Accurate polyp segmentation during colonoscopy examinations can help clinicians locate the polyps which is of great significance in the clinical prevention of colorectal cancer. However, due to large variations in terms of size, color, texture, and morphology of polyps, the similarity between the polyp lesions and their background, the variations of illumination, motion blur, low-contrast areas, intestinal contents during image acquisitions, precisely polyp segmentation is still an open issue. To overcome the challenges above, a new double U-shaped image segmentation network combining convolution structure and Shifted Windows (Swin) Transformer, namely CSwinDoubleU-Net, is proposed in this paper. The developed CSwinDoubleU-Net consists of two U-shaped encode-decode structures associated with a pure CNN-based structure and a pure Transformer-based one, and a feature fusion module that interacts with the two U-shaped parts. The first U-shaped codecs network utilizes the multi-convolution layers to extract the local feature information and the coordinate attention module in each skip connection to effectively reduce the loss of spatial information and obtain the accurate position information of the encoded features. Next, the second U-shaped codecs network uses the Swin Transformer layers with the sliding window to extract global feature information further. At last, a convolutional feature and self-attention feature fusion module (CSFFM) is designed to deeply fuse the local convolutional features extracted from the first U-shape structure and the global self-Attention features extracted from the second U-shape structure. The obtained multi-category and multi-dimensional fused feature information can help to recover the boundary features of polyps. Extensive experiments are conducted to validate the proposed CSwinDoubleU-Net on five publicly available datasets, including CVC-ClinicDB, Kvasir, CVC-ColonDB, CVC-T, and ETIS-Larib. The results show that the proposed model can outperform some state-of-the-art methods and achieve high segmentation performance for polyp images.

CAFE-Net: Cross-Attention and Feature Exploration Network for polyp segmentation

Colorectal polyp segmentation can help physicians screen colonoscopy images, which is essential for preventing colorectal cancer. The segmentation of polyps encounters multiple challenges, like small size, uneven brightness, blurred edges, and the potential confusion of folds with objects. Although existing methods have shown promising performance in addressing these challenges, there are three main shortcomings: (1) during the segmentation process, small polyp objects are lost, (2) the decoder stage faces challenges in restoring fine-grained details of the features, and (3) the limited capacity to aggregate multi-scale features. We propose a cross-attention and feature exploration network (CAFE-Net) for polyp segmentation to address these challenges. The work offers the following contributions: (1) a feature supplement and exploration module (FSEM) supplements in missing details and explores latent features, (2) a cross-attention decoder module (CADM) effectively preserves features from lower layers and restores fine-grained information, and (3) a multi-scale feature aggregation (MFA) module maximizes the utilization of previously learned features. We conducted extensive experiments and compared CAFE-Net with nine state-of-the-art (SOTA) methods. The CAFE-Net demonstrates the best segmentation accuracy across multiple polyp datasets as well as has an obvious advantage in segmenting small polyp objects.

Nonlinear Activation-Free Contextual Attention Network for Polyp Segmentation

The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. In addition, the number of traditional UNet network parameters is large and the segmentation effect is average. To overcome these problems, an innovative nonlinear activation-free uncertainty contextual attention network is proposed in this paper. Based on the UNet network, an encoder and a decoder are added to predict the saliency map of each module in the bottom-up flow and pass it to the next module. We use Res2Net as the backbone network to extract image features, enhance image features through simple parallel axial channel attention, and obtain high-level features with global semantics and low-level features with edge details. At the same time, a nonlinear n on-activation network is introduced, which can reduce the complexity between blocks, thereby further enhancing image feature extraction. This work conducted experiments on five commonly used polyp segmentation datasets, and the experimental evaluation metrics from the mean intersection over union, mean Dice coefficient, and mean absolute error were all improved, which can show that our method has certain advantages over existing methods in terms of segmentation performance and generalization performance.

TranSEFusionNet: Deep fusion network for colorectal polyp segmentation

Fully convolutional neural (FCN) networks like U-Net have been the state-of-the-art methods in colorectal polyp segmentation. However, U-Net still has some limitations in modelling remote semantic information, especially since the semantic information at different levels can vary greatly, making it difficult to utilize this information fully. To address these issues, we propose a new network architecture called TranSEFusionNet that utilizes the Transformer’s global modelling capability to better focus on global contextual semantic information. In addition, we added two feature fusion modules, a spatial feature fusion module (SFM) and an edge feature fusion module (EFM), to the network. SEF with a skip connection can improve the accuracy of passing deep features to shallow features. EFM in the output part of each decoder layer improves the recognition of edge ambiguous features by refining the semantic information of the network. We validate the model’s performance on five publicly available colorectal polyp datasets, and the experiments show that TranSEFusionNet has higher segmentation accuracy. To measure the generalization ability of TranSEFusionNet, we further applied the model to the cell nuclei dataset, which further verifies the performance of our model.Code: https://github.com/Linaaalin/TranSEFusionNet.