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学者姓名：童同
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Abstract ：
The goal of efficient and effective real-world image super-resolution (Real-ISR) is to recover the high-resolution image from the given low-resolution image with unknown degradation by limited computation resources. Prior research has attempted to design a fully degradation-adaptive network, where the entire backbone is a nonlinear combination of several sub-networks which can handle different degradation subspaces. However, these methods heavily rely on expensive dynamic convolution operations and are inefficient in super-resolving images of different degradation levels. To address this issue, we propose an efficient and effective real-world image super-resolution network (E2-RealSR) based on partial degradation modulation, which is consisted of a small regression and a lightweight super-resolution network. The former accurately predicts the individual degradation parameters of input images, while the latter only modulates its partial parameters based on the degradation information. The extensive experiments validate that our proposed method is capable of recovering the rich details in real-world images with varying degradation levels. Moreover, our approach also has an advantage in terms of efficiency, compared to state-of-the-art methods. Our method shows improved performance while only using 20% of the parameters and 60% of the FLOPs of DASR. The relevant code is made available on this link as open source.
Keyword ：
Degradation prediction Degradation prediction Efficient and effective super-resolution Efficient and effective super-resolution Partial degradation modulation Partial degradation modulation Real-world image super-resolution Real-world image super-resolution
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| GB/T 7714 | Zhang, Jiajun , Zhou, Yuanbo , Tong, Tong et al. E2-RealSR: efficient and effective real-world super-resolution network based on partial degradation modulation [J]. | VISUAL COMPUTER , 2024 , 40 (12) : 8867-8880 . |
| MLA | Zhang, Jiajun et al. "E2-RealSR: efficient and effective real-world super-resolution network based on partial degradation modulation" . | VISUAL COMPUTER 40 . 12 (2024) : 8867-8880 . |
| APA | Zhang, Jiajun , Zhou, Yuanbo , Tong, Tong , Liu, Hongjun , Tian, Tian , Hu, Xingmei et al. E2-RealSR: efficient and effective real-world super-resolution network based on partial degradation modulation . | VISUAL COMPUTER , 2024 , 40 (12) , 8867-8880 . |
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High Dynamic Range (HDR) images have more powerful image expression capabilities and better visual quality than Low Dynamic Range (LDR) images, which can sufficiently represent real -world scenes, and will be widely used in the field of film and television in future. However, it is a very challenging task to generate an HDR image from a single -exposure LDR image. In this work, we propose a novel learning -based network, DEUNet, to reconstruct single -frame HDR image with simultaneous denoising and detail reconstruction. The proposed framework consists of two feature extraction branches, which can learn the brightness information and texture information separately for HDR image reconstruction. Each network branch is based on the UNet network structure and the two branches are interacted via spatial feature transformation. As a result, the proposed network can make full use of the multi -scale information at different levels of the image. In addition to the two encoding branches for feature extraction, the proposed network consists of another decoding network for fusing image brightness information and texture information, and a weighting network that selectively preserves most useful information. Compared with state-of-the-art methods, DEUNet can better reduce image noise while reconstructing the details in both the high and the low exposure areas. Experiments have shown that the proposed method achieves state-of-the-art performance on public datasets, indicating the effectiveness of the proposed method in this study.
Keyword ：
Deep learning Deep learning HDR reconstruction HDR reconstruction Image denoising Image denoising Image enhancement Image enhancement
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| GB/T 7714 | Yao, Zhiping , Bi, Jiang , Deng, Wei et al. DEUNet: Dual-encoder UNet for simultaneous denoising and reconstruction of single HDR image [J]. | COMPUTERS & GRAPHICS-UK , 2024 , 119 . |
| MLA | Yao, Zhiping et al. "DEUNet: Dual-encoder UNet for simultaneous denoising and reconstruction of single HDR image" . | COMPUTERS & GRAPHICS-UK 119 (2024) . |
| APA | Yao, Zhiping , Bi, Jiang , Deng, Wei , He, Wenlin , Wang, Zihan , Kuang, Xu et al. DEUNet: Dual-encoder UNet for simultaneous denoising and reconstruction of single HDR image . | COMPUTERS & GRAPHICS-UK , 2024 , 119 . |
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Deep learning models have demonstrated remarkable performance in various biomedical image segmentation tasks. However, their reliance on a large amount of labeled data for training poses challenges as acquiring well-annotated data is expensive and time-consuming. To address this issue, semi-supervised learning (SSL) has emerged as a potential solution to leverage abundant unlabeled data. In this paper, we propose a simple yet effective consistency regularization scheme called Rectified Pyramid Unsupervised Consistency (RPUC) for semi-supervised 3D biomedical image segmentation. Our RPUC adopts a pyramid-like structure by incorporating three segmentation networks. To fully exploit the available unlabeled data, we introduce a novel pyramid unsupervised consistency (PUC) loss, which enforces consistency among the outputs of the three segmentation models and facilitates the transfer of cyclic knowledge. Additionally, we perturb the inputs of the three networks with varying ratios of Gaussian noise to enhance the consistency of unlabeled data outputs. Furthermore, three pseudo labels are generated from the outputs of the three segmentation networks, providing additional supervision during training. Experimental results demonstrate that our proposed RPUC achieves state-of-the-art performance in semi-supervised segmentation on two publicly available 3D biomedical image datasets.
Keyword ：
Rectified pyramid consistency Rectified pyramid consistency Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Zhou, Xiaogen , Li, Zhiqiang , Tong, Tong . RPUC: Semi-supervised 3D Biomedical Image Segmentation Through Rectified Pyramid Unsupervised Consistency [J]. | NEURAL INFORMATION PROCESSING, ICONIP 2023, PT III , 2024 , 14449 : 328-339 . |
| MLA | Zhou, Xiaogen et al. "RPUC: Semi-supervised 3D Biomedical Image Segmentation Through Rectified Pyramid Unsupervised Consistency" . | NEURAL INFORMATION PROCESSING, ICONIP 2023, PT III 14449 (2024) : 328-339 . |
| APA | Zhou, Xiaogen , Li, Zhiqiang , Tong, Tong . RPUC: Semi-supervised 3D Biomedical Image Segmentation Through Rectified Pyramid Unsupervised Consistency . | NEURAL INFORMATION PROCESSING, ICONIP 2023, PT III , 2024 , 14449 , 328-339 . |
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In the field of computer-aided diagnosis (CAD) for spinal diseases, the fundamental task of multi-label segmentation for vertebrae and intervertebral discs (IVDs) assumes a significant role. However, the distinctive characteristics inherent to the spinal structure pose considerable challenges to the segmentation process, impeding its practical applicability in clinical settings. Convolutional neural networks have been widely used in this task; however, their limited receptive field restricts their capacity to capture extended-range spatial correlations. Consequently, the model’s ability to accurately delineate vertebral boundaries is compromised, leading to a notable deterioration in the quality of segmentation outputs. To address this limitation, we propose a novel two-stage convolutional neural network (CNN) framework that incorporates both 3D Transformers and 2D CNNs. By synergistically leveraging the advantages of Transformers in facilitating the integration of long-range dependencies and the ability of CNNs to learn global and local features, our proposed approach exhibits promising potential in enhancing the segmentation performance for vertebrae and intervertebral discs. Moreover, we introduce a graph convolution module into our network architecture to exploit the inherent spatial dependencies present in MRI scans of spinal structures, thereby extracting semantic feature representations and further augmenting the efficacy of segmentation. The evaluation of our proposed method is conducted on the MRSpineSeg Challenge dataset, encompassing T2-weighted MR images. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Combined with 3D Transformers and 2D CNN Combined with 3D Transformers and 2D CNN deep learning deep learning Graph Convolution module Graph Convolution module multi-label multi-label Segmentation of Vertebrae and Intervertebral Discs Segmentation of Vertebrae and Intervertebral Discs Two-stage Two-stage
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| GB/T 7714 | Li, Z. , Zhou, X. , Tong, T. . A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN [未知]. |
| MLA | Li, Z. et al. "A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN" [未知]. |
| APA | Li, Z. , Zhou, X. , Tong, T. . A Two-Stage Network for Segmentation of Vertebrae and Intervertebral Discs: Integration of Efficient Local-Global Fusion Using 3D Transformer and 2D CNN [未知]. |
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Lymphoma is a malignant tumor originating from the lymphohematopoietic system. At present, pathological evaluation is one of the important methods to diagnose malignant lymphoma. In clinical practice, the diagnosis of lymphoma, especially in newly diagnosed patients, depends mainly on histopathological examination of the lesion. The type of lymphoma is determined by repeatedly comparing hematoxylin-eosin (H&E) whole slide images (WSIs) and immunohistochemical WSIs under a microscope. It is a repetitive, tedious, and time-consuming process. Therefore, it is extremely important to establish a highly accurate and standardized lymphoma diagnosis algorithm. In this paper, we developed an innovative deep -learning framework based on multi -model fusion, which only uses the H&E slides, with special attention to gastric Mucosa-associated lymphoid tissue (MALT) lymphoma diagnosis. The proposed framework can evaluate and improve the auxiliary ability of the convolutional neural network (CNN) in clinical practice for the diagnosis of gastric MALT lymphoma. The proposed method achieved an accuracy of 98.53% using image patches and an accuracy of 94.96% on 258 WSIs. These results show the high accuracy in the diagnosis of MALT lymphoma and its potential use in clinical practice. In addition, we also estimated the 95% confidence interval of WSIs prediction values. The result shows that the proposed framework has a high degree of differentiation in the interpretation between gastric MALT lymphoma and normal pathological tissues.
Keyword ：
Artificial intelligence Artificial intelligence CNN CNN Deep learning Deep learning MALT lymphoma MALT lymphoma Model fusion Model fusion
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| GB/T 7714 | Quan, Jiawei , Ye, Jingxuan , Lan, Junlin et al. A deep learning model fusion algorithm for the diagnosis of gastric Mucosa-associated lymphoid tissue lymphoma [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
| MLA | Quan, Jiawei et al. "A deep learning model fusion algorithm for the diagnosis of gastric Mucosa-associated lymphoid tissue lymphoma" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 92 (2024) . |
| APA | Quan, Jiawei , Ye, Jingxuan , Lan, Junlin , Wang, Jianchao , Hu, Ziwei , Guo, Zhechen et al. A deep learning model fusion algorithm for the diagnosis of gastric Mucosa-associated lymphoid tissue lymphoma . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
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Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resolution can be applied to enhance stereo images, these methods often introduce notable modifications to the inherent disparity, resulting in a loss in the consistency of disparity between the original and the enhanced stereo images. To overcome this limitation, this paper proposes a novel approach that integrates an implicit stereo information discriminator and a hybrid degradation model. This combination ensures effective enhancement while preserving disparity consistency. The proposed method bridges the gap between the complex degradations in real-world stereo domain and the simpler degradations in real-world single-image super-resolution domain. Our results demonstrate impressive performance on synthetic and real datasets, enhancing visual perception while maintaining disparity consistency. © 2024 Elsevier Ltd
Keyword ：
Disparity Disparity Real-world Real-world Stereo image super-resolution Stereo image super-resolution Visual perception Visual perception
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| GB/T 7714 | Zhou, Y. , Xue, Y. , Bi, J. et al. Towards real world stereo image super-resolution via hybrid degradation model and discriminator for implied stereo image information [J]. | Expert Systems with Applications , 2024 , 255 . |
| MLA | Zhou, Y. et al. "Towards real world stereo image super-resolution via hybrid degradation model and discriminator for implied stereo image information" . | Expert Systems with Applications 255 (2024) . |
| APA | Zhou, Y. , Xue, Y. , Bi, J. , He, W. , Zhang, X. , Zhang, J. et al. Towards real world stereo image super-resolution via hybrid degradation model and discriminator for implied stereo image information . | Expert Systems with Applications , 2024 , 255 . |
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Ovarian cancer presents a notable health concern characterized by its unfavorable prognosis and elevated mortality rates in the female population. Accurate prognostic assessment is essential for tailoring treatment strategies and improving patient outcomes. Analysis of histopathological whole-slide images is the gold standard for pathological diagnosis, which contains rich phenotypic and molecular information. Multiple instance learning methods have been the dominant approach for processing megapixel whole slide images. However, the methods adopt the one image as a bag strategy, which will contain many noisy tiles leading to model overfitting during training. To mitigate the above situation, we propose a transformer-based multi-instance learning framework with a pseudo-bag strategy (TransPBMIL) for predicting overall survival within 3 years of ovarian cancer patients using pathological images. Extensive studies on multiple cancer prognostic datasets demonstrate the superiority of TransPBMIL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Deep Learning Deep Learning Ovarian Cancer Ovarian Cancer Prognosis Prediction Prognosis Prediction Tissue Pathology Analysis Tissue Pathology Analysis
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| GB/T 7714 | Mao, Y. , Hu, Z. , Zhang, X. et al. TransPBMIL: Transformer-Based Weakly Supervised Prognostic Prediction in Ovarian Cancer with Pseudo-Bag Strategy [未知]. |
| MLA | Mao, Y. et al. "TransPBMIL: Transformer-Based Weakly Supervised Prognostic Prediction in Ovarian Cancer with Pseudo-Bag Strategy" [未知]. |
| APA | Mao, Y. , Hu, Z. , Zhang, X. , Tong, T. . TransPBMIL: Transformer-Based Weakly Supervised Prognostic Prediction in Ovarian Cancer with Pseudo-Bag Strategy [未知]. |
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Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of interest on the gigapixel of whole slide images (WSIs), which however is laborious and often biased. In this paper, we propose a weakly supervised framework based on Tokens-to-Token Vision Transformer (WS-T2T-ViT) for accurate NPC classification with only a slide-level label. The label of tile images is inherited from their slide-level label. Specifically, WS-T2T-ViT is composed of the multi-resolution pyramid, T2T-ViT and multi-scale attention module. The multi-resolution pyramid is designed for imitating the coarse-to-fine process of manual pathological analysis to learn features from different magnification levels. The T2T module captures the local and global features to overcome the lack of global information. The multi-scale attention module improves classification performance by weighting the contributions of different granularity levels. Extensive experiments are performed on the 802-patient NPC and CAMELYON16 dataset. WS-T2T-ViT achieves an area under the receiver operating characteristic curve (AUC) of 0.989 for NPC classification on the NPC dataset. The experiment results of CAMELYON16 dataset demonstrate the robustness and generalizability of WS-T2T-ViT in WSI-level classification. IEEE
Keyword ：
Annotations Annotations Breast cancer Breast cancer Cancer Cancer Digital pathology Digital pathology Feature extraction Feature extraction Hospitals Hospitals Image pyramid Image pyramid Nasopharyngeal carcinoma Nasopharyngeal carcinoma Transformer Transformer Transformers Transformers Tumors Tumors Weakly supervised learning Weakly supervised learning
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| GB/T 7714 | Hu, Z. , Wang, J. , Gao, Q. et al. Weakly Supervised Classification for Nasopharyngeal Carcinoma with Transformer in Whole Slide Images [J]. | IEEE Journal of Biomedical and Health Informatics , 2024 , 28 (12) : 1-12 . |
| MLA | Hu, Z. et al. "Weakly Supervised Classification for Nasopharyngeal Carcinoma with Transformer in Whole Slide Images" . | IEEE Journal of Biomedical and Health Informatics 28 . 12 (2024) : 1-12 . |
| APA | Hu, Z. , Wang, J. , Gao, Q. , Wu, Z. , Xu, H. , Guo, Z. et al. Weakly Supervised Classification for Nasopharyngeal Carcinoma with Transformer in Whole Slide Images . | IEEE Journal of Biomedical and Health Informatics , 2024 , 28 (12) , 1-12 . |
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The fundamental goal of Few-Shot Segmentation (FSS) is to achieve accurate segmentation of irrelevant categories by training the network on a very limited support set of relevant classes. This task requires deep mining and efficient utilization of informative training data, as well as the extraction of fine-grained correspondences between the support set and the query set. To address these challenges, we propose a Multi-Dimensional Information Interaction Network (MDIINet) that explores different dimensions of information correlation and different scales of information correlation. It extracts fine-grained correlation information at different levels from transformed convolutional layers in various dimensions, as well as coarse-grained correlation information at different levels from the concatenation process of transformed scales. This method employs a pyramid structure to differentiate information from coarse to fine in different dimensions, and performs a series of super-correlation operations to integrate fine-grained and coarse-grained information from each level to obtain the final feature output. The effectiveness of the proposed method has been validated on the PASCAL-5i and FSS-100 benchmark few-shot segmentation datasets, and the experimental results show significant performance improvements over state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Metric Learning Metric Learning Multi-dimension Multi-dimension Segment Segment Semantic Aware Semantic Aware
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| GB/T 7714 | Chen, H. , Qiu, X. , Zhou, X. et al. MDIINet: A Few-Shot Semantic Segmentation Network by Exploiting Multi-dimensional Information Interaction [未知]. |
| MLA | Chen, H. et al. "MDIINet: A Few-Shot Semantic Segmentation Network by Exploiting Multi-dimensional Information Interaction" [未知]. |
| APA | Chen, H. , Qiu, X. , Zhou, X. , Wang, T. , Hu, X. , Zhang, Q. et al. MDIINet: A Few-Shot Semantic Segmentation Network by Exploiting Multi-dimensional Information Interaction [未知]. |
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Although deep learning models have demonstrated impressive performance in various biomedical image segmentation tasks, their effectiveness heavily relies on a large amount of annotated training data, which can be costly to acquire. Semi-supervised learning (SSL) methods have emerged as a potential solution to mitigate this challenge by leveraging the abundance of unlabeled data. In this paper, we propose a highly effective SSL method for 3D biomedical image segmentation, called Pyramid Pseudo-Labeling Supervision (PPS). The PPS comprises three segmentation networks, forming a pyramid-like network structure. To enforce consistency in the outputs of the unlabeled data, we introduce a novel rectified pyramid consistency (RPC) loss. The PPS learns from the plentiful unlabeled data by minimizing the RPC loss, which ensures consistency between the pyramid predictions and the cycled pseudo-labeling knowledge among the three segmentation networks. Additionally, weak data augmentation is applied to perturb the inputs, further enhancing the consistency of the unlabeled data outputs. Experimental results demonstrate that our method achieves state-of-the-art performance on two publicly available 3D biomedical image datasets.
Keyword ：
Rectified pyramid consistency Rectified pyramid consistency Semi-supervised biomedical image segmentation Semi-supervised biomedical image segmentation
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| GB/T 7714 | Zhou, Xiaogen , Li, Zhiqiang , Tong, Tong . PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XIII , 2024 , 14437 : 272-283 . |
| MLA | Zhou, Xiaogen et al. "PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XIII 14437 (2024) : 272-283 . |
| APA | Zhou, Xiaogen , Li, Zhiqiang , Tong, Tong . PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT XIII , 2024 , 14437 , 272-283 . |
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