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学者姓名：童同
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Abstract ：
Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts. However, noise will reduce the quality of MR images, and then affect the doctor's diagnosis of diseases. Existing filtering methods, transform-domain methods, statistical methods and Convolutional Neural Network (CNN) methods main aim to denoise individual sequences of images without considering the relationships between multiple different sequences. They cannot balance the extraction of high-dimensional and low-dimensional features in MR images, and hard to maintain a good balance between preserving image texture details and denoising strength. To overcome these challenges, this work proposes a controllable Multimodal Cross-Global Learnable Attention Network (MMCGLANet) for MR image denoising with Arbitrary Modal Missing. Specifically, Encoder is employed to extract the shallow features of the image which share weight module, and Convolutional Long Short-Term Memory(ConvLSTM) is employed to extract the associated features between different frames within the same modal. Cross Global Learnable Attention Network(CGLANet) is employed to extract and fuse image features between multimodal and within the same modality. In addition, sequence code is employed to label missing modalities, which allows for Arbitrary Modal Missing during model training, validation, and testing. Experimental results demonstrate that our method has achieved good denoising results on different public and real MR image dataset.
Keyword ：
Arbitrary modal missing Arbitrary modal missing Controllable Controllable Cross global attention Cross global attention Multimodal fusion Multimodal fusion Multimodal MR image denoising Multimodal MR image denoising
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| GB/T 7714 | Jiang, Mingfu , Wang, Shuai , Chan, Ka-Hou et al. Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing [J]. | COMPUTERIZED MEDICAL IMAGING AND GRAPHICS , 2025 , 121 . |
| MLA | Jiang, Mingfu et al. "Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing" . | COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 121 (2025) . |
| APA | Jiang, Mingfu , Wang, Shuai , Chan, Ka-Hou , Sun, Yue , Xu, Yi , Zhang, Zhuoneng et al. Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing . | COMPUTERIZED MEDICAL IMAGING AND GRAPHICS , 2025 , 121 . |
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Video super-resolution (VSR) aims to restore high-resolution (HR) frames from low-resolution (LR) frames, the key to this task is to fully utilize the complementary information between frames to reconstruct high- resolution sequences. Current works tackle with this by exploiting a sliding window strategy or a recurrent architecture for single alignment, which either lacks long range modeling ability or is prone to frame-by-frame error accumulation. In this paper, we propose a Multi-layer Hybrid Alignment network for VSR (MHAVSR), which combines a sliding window with a recurrent structure and extends the number of propagation layers based on this hybrid structure. Repeatedly, at each propagation layer, alignment operations are performed simultaneously on bidirectional neighboring frames and hidden states from recursive propagation, which improves the alignment while fully utilizing both the short-term and long-term information in the video sequence. Next, we present a flow-enhanced dual-deformable alignment module, which improves the accuracy of deformable convolutional offsets by optical flow and fuses the separate alignment results of the hybrid alignment to reduce the artifacts caused by alignment errors. In addition, we introduce a spatial-temporal reconstruction module to compensate the representation capacity of model at different scales. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. In particular, on the Vid4 test set, our model exceeds the IconVSR by 0.82 dB in terms of PSNR with a similar number of parameters. Codes are available at https://github.com/fzuqxt/MHAVSR.
Keyword ：
Deformable convolution Deformable convolution Hybrid propagation Hybrid propagation Long-short term information Long-short term information Multi-layer alignment Multi-layer alignment Video super-resolution Video super-resolution
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| GB/T 7714 | Qiu, Xintao , Zhou, Yuanbo , Zhang, Xinlin et al. MHAVSR: A multi-layer hybrid alignment network for video super-resolution [J]. | NEUROCOMPUTING , 2025 , 624 . |
| MLA | Qiu, Xintao et al. "MHAVSR: A multi-layer hybrid alignment network for video super-resolution" . | NEUROCOMPUTING 624 (2025) . |
| APA | Qiu, Xintao , Zhou, Yuanbo , Zhang, Xinlin , Xue, Yuyang , Lin, Xiaoyong , Dai, Xinwei et al. MHAVSR: A multi-layer hybrid alignment network for video super-resolution . | NEUROCOMPUTING , 2025 , 624 . |
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Although diffusion prior-based single-image super-resolution has demonstrated remarkable reconstruction capabilities, its potential in the domain of stereo image super-resolution remains underexplored. One significant challenge lies in the inherent stochasticity of diffusion models, which makes it difficult to ensure that the generated left and right images exhibit high semantic and texture consistency. This poses a considerable obstacle to advancing research in this field. Therefore, We introduce DiffSteISR, a pioneering framework for reconstructing real-world stereo images. DiffSteISR utilizes the powerful prior knowledge embedded in pre-trained text-to-image model to efficiently recover the lost texture details in low-resolution stereo images. Specifically, DiffSteISR implements a time-aware stereo cross attention with temperature adapter (TASCATA) to guide the diffusion process, ensuring that the generated left and right views exhibit high texture consistency thereby reducing disparity error between the super-resolved images and the ground truth (GT) images. Additionally, a stereo omni attention control network (SOA ControlNet) is proposed to enhance the consistency of super-resolved images with GT images in the pixel, perceptual, and distribution space. Finally, DiffSteISR incorporates a stereo semantic extractor (SSE) to capture unique viewpoint soft semantic information and shared hard tag semantic information, thereby effectively improving the semantic accuracy and consistency of the generated left and right images. Extensive experimental results demonstrate that DiffSteISR accurately reconstructs natural and precise textures from low-resolution stereo images while maintaining a high consistency of semantic and texture between the left and right views.
Keyword ：
ControlNet ControlNet Diffusion model Diffusion model Reconstructing Reconstructing Stereo image super-resolution Stereo image super-resolution Texture consistency Texture consistency
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| GB/T 7714 | Zhou, Yuanbo , Zhang, Xinlin , Deng, Wei et al. DiffSteISR: Harnessing diffusion prior for superior real-world stereo image super-resolution [J]. | NEUROCOMPUTING , 2025 , 623 . |
| MLA | Zhou, Yuanbo et al. "DiffSteISR: Harnessing diffusion prior for superior real-world stereo image super-resolution" . | NEUROCOMPUTING 623 (2025) . |
| APA | Zhou, Yuanbo , Zhang, Xinlin , Deng, Wei , Wang, Tao , Tan, Tao , Gao, Qinquan et al. DiffSteISR: Harnessing diffusion prior for superior real-world stereo image super-resolution . | NEUROCOMPUTING , 2025 , 623 . |
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BackgroundFirst-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), whereas deep learning (DL) can predict HER2 status based on tumor histopathological features. However, it remains uncertain whether these deep learning-derived features can predict the efficacy of anti-HER2 therapy.MethodsWe analyzed a cohort of 300 consecutive surgical specimens and 101 biopsy specimens, all undergoing HER2 testing, along with 41 biopsy specimens receiving trastuzumab-based therapy for HER2-positive GAC.ResultsWe developed a convolutional neural network (CNN) model using surgical specimens that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification, and achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. The model also predicted HER2 status in gastric biopsy specimens, achieving an AUC of 0.723. Furthermore, our classifier was trained using 41 HER2-positive gastric biopsy specimens that had undergone trastuzumab treatment, our model demonstrated an AUC of 0.833 for the (CR + PR) / (SD + PD) subgroup.ConclusionThis work explores an algorithm that utilizes hematoxylin and eosin (H&E) staining to accurately predict HER2 status and assess the response to trastuzumab in GAC, potentially facilitating clinical decision-making.
Keyword ：
Deep learning Deep learning Efficacy Efficacy Gastric adenocarcinoma Gastric adenocarcinoma HER2 HER2 Trastuzumab Trastuzumab
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| GB/T 7714 | Wu, Zhida , Wang, Tao , Lan, Junlin et al. Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features [J]. | JOURNAL OF TRANSLATIONAL MEDICINE , 2025 , 23 (1) . |
| MLA | Wu, Zhida et al. "Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features" . | JOURNAL OF TRANSLATIONAL MEDICINE 23 . 1 (2025) . |
| APA | Wu, Zhida , Wang, Tao , Lan, Junlin , Wang, Jianchao , Chen, Gang , Tong, Tong et al. Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features . | JOURNAL OF TRANSLATIONAL MEDICINE , 2025 , 23 (1) . |
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The degradation types of snow are complex and diverse. Existing methods employ sophisticated model architectures to model sufficient visual representations for snow removal. In order to remove snow more efficiently, inspired by the powerful visual representations of pre-trained large models and the efficient parameter fine-tuning paradigm in the field of natural language processing, we have pioneered the exploration of applying efficient parameter fine-tuning in low-level vision. Taking the desnowing task as the starting point, we introduced TuneSnow, a framework for efficient parameter fine-tuning that can be integrated with desnowing network to improve desnowing performance. Initially, we introduced Hybrid Adapters for the efficient fine-tuning of pre-trained vision models. We then proposed a Progressive Multi-Scale Perception module (PMSP) to harness the feature representation potential of pre-trained models. Finally, we presented a Degraded Area Restoration module (DAR) based on Multi-Scale Fusion Refinement module (MSFR) to recovery details after desnowing. Extensive experiments demonstrate that our approach trains only 15% of the parameters and delivers state-of-the-art performance on multiple publicly available datasets. TuneSnow can serve as a plug-and-play component to enhance the performance of other U-shaped image restoration models, including derain, dehaze, deblur, and more. The code and datasets in this study are available at https://github.com/dxw2000/PEFT-TuneSnow.
Keyword ：
Parameter-efficient fine-tuning Parameter-efficient fine-tuning Pre-trained model Pre-trained model Segment anything model Segment anything model Snow removal Snow removal
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| GB/T 7714 | Dai, Xinwei , Zhou, Yuanbo , Qiu, Xintao et al. Parameter-efficient fine-tuning for single image snow removal [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 265 . |
| MLA | Dai, Xinwei et al. "Parameter-efficient fine-tuning for single image snow removal" . | EXPERT SYSTEMS WITH APPLICATIONS 265 (2025) . |
| APA | Dai, Xinwei , Zhou, Yuanbo , Qiu, Xintao , Tang, Hui , Tong, Tong . Parameter-efficient fine-tuning for single image snow removal . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 265 . |
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Medical image segmentation is a critical and complex process in medical image processing and analysis. With the development of artificial intelligence, the application of deep learning in medical image segmentation is becoming increasingly widespread. Existing techniques are mostly based on the U-shaped convolutional neural network and its variants, such as the U-Net framework, which uses skip connections or element-wise addition to fuse features from different levels in the decoder. However, these operations often weaken the compatibility between features at different levels, leading to a significant amount of redundant information and imprecise lesion segmentation. The construction of the loss function is a key factor in neural network design, but traditional loss functions lack high domain generalization and the interpretability of domain-invariant features needs improvement. To address these issues, we propose a Bayesian loss-based Multi-Scale Subtraction Attention Network (MSAByNet). Specifically, we propose an inter-layer and intra-layer multi-scale subtraction attention module, and different sizes of receptive fields were set for different levels of modules to avoid loss of feature map resolution and edge detail features. Additionally, we design a multi-scale deep spatial attention mechanism to learn spatial dimension information and enrich multi-scale differential information. Furthermore, we introduce Bayesian loss, re-modeling the image in spatial terms, enabling our MSAByNet to capture stable shapes, improving domain generalization performance. We have evaluated our proposed network on two publicly available datasets: the BUSI dataset and the Kvasir-SEG dataset. Experimental results demonstrate that the proposed MSAByNet outperforms several state-of-the-art segmentation methods. The codes are available at https://github.com/zlxokok/MSAByNet.
Keyword ：
Bayesian loss Bayesian loss Deep convolutional neural networks Deep convolutional neural networks Deep learning Deep learning Medical image segmentation Medical image segmentation Multi-scale processing Multi-scale processing
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| GB/T 7714 | Zhao, Longxuan , Wang, Tao , Chen, Yuanbin et al. MSAByNet: A multiscale subtraction attention network framework based on Bayesian loss for medical image segmentation [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 103 . |
| MLA | Zhao, Longxuan et al. "MSAByNet: A multiscale subtraction attention network framework based on Bayesian loss for medical image segmentation" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 103 (2025) . |
| APA | Zhao, Longxuan , Wang, Tao , Chen, Yuanbin , Zhang, Xinlin , Tang, Hui , Zong, Ruige et al. MSAByNet: A multiscale subtraction attention network framework based on Bayesian loss for medical image segmentation . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 103 . |
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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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Purpose: The aim of this study was to investigate the causal relationship between low-density lipoprotein cholesterol (LDL-C) and five cancers (breast, cervical, thyroid, prostate and colorectal) using the Mendelian Randomization (MR) method, with a view to revealing the potential role of LDL-C in the development of these cancers. Methods: We used gene variant data and disease data from the Genome-Wide Association Study (GWAS) database to assess the causal relationship between LDL-C and each cancer by Mendelian randomisation analysis methods such as inverse variance weighting and MR-Egger. Specifically, we selected Proprotein convertase subtilisin/kexin type 9 (PCSK9) and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), genes associated with LDL-C levels, as instrumental variables, extracted the corresponding single nucleotide polymorphism (SNP) data and analysed the associations of these SNPs with five cancers.In addition, sensitivity analyses and heterogeneity tests were performed to ensure the reliability of the results. Results: The analyses showed that when using HMGCR gene, LDL-C were significantly and positively associated with breast (OR:1.200, 95% CI:1.082–1.329, p = 0.001), prostate (OR:1.198, 95% CI:1.050–1.366, p = 0.007), and thyroid cancers (OR:8.291, 95% CI:3.189- 21.555, p = 0.00001) were significantly positively correlated, whereas they were significantly negatively correlated with colorectal cancer (OR:0.641, 95% CI:0.442–0.928, p = 0.019); the results for cervical cancer were not significant (p = 0.050). When using the PCSK9 gene, LDL-C levels were significantly and positively associated with breast (OR:1.107, 95%:CI 1.031–1.187, p = 0.005) and prostate (OR:1.219, 95%:CI 1.101–1.349, p = 0.0001) cancers, but not with cervical (p = 0.294), thyroid cancer (p = 0.759) and colorectal cancer (p = 0.572). Conclusion: Analyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a potential risk factor for breast and prostate cancer, while analyses of the HMGCR gene have also suggested that LDL-C may increase the risk of thyroid cancer and decrease the risk of colorectal cancer. © The Author(s) 2024.
Keyword ：
Cancer Cancer Causality Causality HMGCR HMGCR LDL cholesterol LDL cholesterol Mendelian randomization Mendelian randomization PSCK9 PSCK9
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| GB/T 7714 | Liang, H. , Tang, C. , Sun, Y. et al. Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers [J]. | Discover Oncology , 2024 , 15 (1) . |
| MLA | Liang, H. et al. "Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers" . | Discover Oncology 15 . 1 (2024) . |
| APA | Liang, H. , Tang, C. , Sun, Y. , Wang, M. , Tong, T. , Gao, Q. et al. Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers . | Discover Oncology , 2024 , 15 (1) . |
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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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Types of snow degradation are complex and diverse. Snow removal often requires the construction of sufficient visual representations. Although convolution-based methods perform well in local perception, they struggle to model globally. On the other hand, methods based on self-attention can capture long-range dependencies but often overlook local information and texture details. In this paper, we proposed a hybrid network called WaveFrSnow, aimed at enhancing the performance of single-image snow removal by combining the advantages of convolution and cross-attention. Firstly, we introduced a frequency-separation cross-attention mechanism based on wavelet transform (WaveFrSA) to enhance the global and texture representations of snow removal. Specifically, frequency-separated attention perceives the texture in the high-frequency branch, captures global information in the low-frequency branch, and introduces convolution to obtain local features. In addition, we constructed local representations through efficient convolutional encoder branches. Furthermore, we develop a M ulti-Scale S cale D egradation A ggregation (MSDA) module to integrate rich implicit degradation features. Based on the MSDA module, a D egradation A rea R estoration (DAR) network is constructed, aiming to achieve high-quality image restoration following the snow removal process. Taken together, comprehensive experimental results on multiple publicly available datasets demonstrate the superiority of the proposed method over the state-of-the-art method. Additionally, the desnowing results effectively improve the accuracy of downstream vision tasks. The code and datasets in this study are available at https://github.com/dxw2000/WaveFrSnow.
Keyword ：
Frequency domain attention Frequency domain attention Hybrid model Hybrid model Image snow removal Image snow removal Wavelet transform Wavelet transform
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| GB/T 7714 | Dai, Xinwei , Zhou, Yuanbo , Qiu, Xintao et al. WaveFrSnow: Comprehensive perception wavelet transform frequency separation transformer for image snow removal [J]. | DIGITAL SIGNAL PROCESSING , 2024 , 155 . |
| MLA | Dai, Xinwei et al. "WaveFrSnow: Comprehensive perception wavelet transform frequency separation transformer for image snow removal" . | DIGITAL SIGNAL PROCESSING 155 (2024) . |
| APA | Dai, Xinwei , Zhou, Yuanbo , Qiu, Xintao , Tang, Hui , Tan, Tao , Zhang, Qing et al. WaveFrSnow: Comprehensive perception wavelet transform frequency separation transformer for image snow removal . | DIGITAL SIGNAL PROCESSING , 2024 , 155 . |
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