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学者姓名：高钦泉
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Background: Medical imaging is fundamental to modern clinical diagnostics, providing essential insights for disease detection and treatment planning. However, variations in imaging equipment, protocols, and conditions across institutions lead to inconsistencies in image quality, which hinders diagnostic accuracy and the performance of machine learning models. Although existing harmonization techniques improve image uniformity, they often result in the loss of critical image details. This study presents novel singular value decomposition (SVD)-based harmonization and de-harmonization algorithms, designed to address these challenges by ensuring consistency across diverse imaging conditions, while preserving essential diagnostic information. Methods: The proposed approach utilizes SVD to decompose medical images into multiple frequency bands, allowing for frequency-specific adjustment that enhances both high-frequency details and low-frequency uniformity. The harmonization process begins by splitting red, green, blue (RGB) images into individual channels and applying SVD to extract principal components, enabling the selective enhancement of clinically relevant structures while mitigating variability in brightness and contrast. The de-harmonization method, in contrast, strategically subtracts high-frequency components to remove unwanted noise and preserve significant details. A novel integration of harmonization and de-harmonization processes is employed to optimize image clarity and diagnostic utility. The method’s robustness was evaluated through extensive experimentation, including homology (training and testing on the same dataset) and heterology (training on one dataset and testing on a different dataset) experiments. These tests were conducted across multiple datasets—handwritten digit classification (MNIST, USPS), retinal image segmentation [Digital Retinal Images for Vessel Extraction (DRIVE), Choroidal Artery Segmentation Database (CHASE_DB1)], and breast cancer detection (RSNAbreast, INbreast)—with deep learning models employed for performance evaluation. Results: The SVD harmonization and de-harmonization algorithms outperformed traditional methods in image quality and computational efficiency. In homology tests, they achieved 99.21% accuracy on MNIST and 98.7% on USPS. In heterology tests, they scored 98.7% on USPS (trained on MNIST) and 98.46% on MNIST (trained on USPS). For retinal vessel segmentation, AUCs reached 0.976 on DRIVE and 0.982 on CHASE_DB1. For breast cancer detection, AUCs were 0.934 on RSNAbreast and 0.921 on INbreast. Conclusions: The proposed SVD-based harmonization and de-harmonization algorithms present a robust solution to the challenges of image variability in medical imaging. By addressing inconsistencies across different datasets and imaging modalities, while preserving crucial diagnostic information, the techniques enhance the visual quality and clinical utility of medical images. The method’s strong performance in both homology and heterology experiments demonstrates its broad applicability and potential to improve the effectiveness of machine learning models in various medical imaging tasks. © AME Publishing Company.
Keyword ：
deep learning deep learning Harmonization Harmonization medical image processing medical image processing robustness robustness singular value decomposition (SVD) singular value decomposition (SVD)
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| GB/T 7714 | Chen, H. , Li, X. , Chan, K.-H. et al. Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain [J]. | Quantitative Imaging in Medicine and Surgery , 2025 , 15 (8) : 7062-7079 . |
| MLA | Chen, H. et al. "Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain" . | Quantitative Imaging in Medicine and Surgery 15 . 8 (2025) : 7062-7079 . |
| APA | Chen, H. , Li, X. , Chan, K.-H. , Sun, Y. , Wang, R. , Gao, Q. et al. Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain . | Quantitative Imaging in Medicine and Surgery , 2025 , 15 (8) , 7062-7079 . |
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Medical image segmentation is crucial in medical image analysis. In recent years, deep learning, particularly convolutional neural networks (CNNs) and Transformer models, has significantly advanced this field. To fully leverage the abilities of CNNs and Transformers in extracting local and global information, we propose HSINet, which employs Swin Transformer and the newly introduced Deep Dense Feature Extraction (DFE) block to construct dual encoders. A Swin Transformer and DFE Encoded Feature Fusion (TDEF) module is designed to merge features from the two branches, and the Multi-Scale Semantic Fusion (MSSF) module further promotes the full utilization of low-level and high-level features from the encoders. We evaluated the proposed network on the familial cerebral cavernous malformations private dataset (SG-FCCM) and the ISIC-2017 challenge dataset. The experimental results indicate that the proposed HSINet outperforms several other advanced segmentation methods, demonstrating its superiority in medical image segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Convolutional neural networks Convolutional neural networks Deep neural networks Deep neural networks Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Zong, Ruige , Wang, Tao , Zhang, Xinlin et al. HSINet: A Hybrid Semantic Integration Network for Medical Image Segmentation [C] . 2025 : 339-353 . |
| MLA | Zong, Ruige et al. "HSINet: A Hybrid Semantic Integration Network for Medical Image Segmentation" . (2025) : 339-353 . |
| APA | Zong, Ruige , Wang, Tao , Zhang, Xinlin , Gao, Qinquan , Kang, Dezhi , Lin, Fuxin et al. HSINet: A Hybrid Semantic Integration Network for Medical Image Segmentation . (2025) : 339-353 . |
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Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct category. However, this often leads to intra-class collision in a large latent space, compromising the quality of learned representations. To address this issue, we propose a novel contrastive learning method that utilizes randomly generated supervision signals. Our framework incorporates two projection heads: one handles conventional classification tasks, while the other employs a random algorithm to generate fixed-length vectors representing different classes. The second head executes a supervised contrastive learning task based on these vectors, effectively clustering instances of the same class and increasing the separation between different classes. Our method, Contrastive Learning via Randomly Generated Supervision(CLRGS), significantly improves the quality of feature representations across various datasets and achieves state-of-the-art performance in contrastive learning tasks. © 2025 IEEE.
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| GB/T 7714 | Wang, Shibo , Ma, Zili , Chan, Ka-Hou et al. Contrastive Learning via Randomly Generated Deep Supervision [C] . 2025 . |
| MLA | Wang, Shibo et al. "Contrastive Learning via Randomly Generated Deep Supervision" . (2025) . |
| APA | Wang, Shibo , Ma, Zili , Chan, Ka-Hou , Liu, Yue , Tong, Tong , Gao, Qinquan et al. Contrastive Learning via Randomly Generated Deep Supervision . (2025) . |
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Familial cerebral cavernous malformation (FCCM) is a hereditary disorder characterized by abnormal vascular structures within the central nervous system. The FCCM lesions are often numerous and intricate, making quantitative analysis of the lesions a labor-intensive task. Consequently, clinicians face challenges in quantitatively assessing the severity of lesions and determining whether lesions have progressed. To alleviate this problem, we propose a quantitative statistical framework for FCCM, which comprises an efficient annotation module, an FCCM lesion segmentation module, and an FCCM lesion quantitative statistics module. Our framework demonstrates precise segmentation of the FCCM lesion based on efficient data annotation, achieving a Dice coefficient of 91.09%. More importantly, we focus on 3D quantitative statistics of lesions, which is combined with image registration to realize the quantitative comparison of lesions between different examinations of patients. A visualization framework has also been established for doctors to comprehensively compare and analyze lesions. The experimental results have demonstrated that our proposed framework not only obtains objective, accurate, and comprehensive quantitative statistical information, which provides a quantitative assessment method for disease progression and drug efficacy study, but also considerably reduces the manual measurement and statistical workload of lesions. This highlights the potential of practical application of the framework in FCCM clinical research and clinical decision-making.
Keyword ：
Annotations Annotations Data annotation Data annotation Deep learning Deep learning Diseases Diseases familial cerebral cavernous malformation familial cerebral cavernous malformation image registration image registration Image segmentation Image segmentation Lesions Lesions Magnetic resonance imaging Magnetic resonance imaging Medical diagnostic imaging Medical diagnostic imaging medical image segmentation medical image segmentation Medical services Medical services quantitative statistics quantitative statistics Statistical analysis Statistical analysis Training Training
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| GB/T 7714 | Zong, Ruige , Wang, Tao , Li, Chunwang et al. Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations [J]. | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING , 2025 , 72 (7) : 2269-2282 . |
| MLA | Zong, Ruige et al. "Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations" . | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 72 . 7 (2025) : 2269-2282 . |
| APA | Zong, Ruige , Wang, Tao , Li, Chunwang , Zhang, Xinlin , Chen, Yuanbin , Zhao, Longxuan et al. Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations . | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING , 2025 , 72 (7) , 2269-2282 . |
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With the rapid advancement of automation and intelligence in the electronics manufacturing industry, the throughput of a single production line was grown exponentially. Although high efficiency brought significant cost and time advantages, it also led to two major challenges: (1) extremely low tolerance for error-any slight defect might have caused the entire product to be scrapped; (2) increasingly diverse and more concealed types of defects-bubble defects, internal chip defects, printed circuit board (PCB) defects, and specific process defects were continuously emerged, posing significant challenges to the inspection process. Traditional manual visual inspection or single-task deep learning models were often struggled to balance detection efficiency and accuracy in complex industrial scenarios. To address the above challenges, a single-stage industrial defect detection model based on multi-dataset mixed training-MSAN-Net-was proposed in this paper. Representative datasets covering the typical scenarios mentioned above were collected and organized, and part of the data was re-annotated to ensure a high level of consistency with actual production environments. MSAN-Net was adopted an integrated architecture, deeply combining UnifiedViT, C2f modules, convolution operations, SPPF structure, and Bi-Level Routing Attention mechanism to achieve accurate identification of complex industrial defects. Extensive experiments (including comparisons with multiple methods, ablation studies, and external validation) showed that MSAN-Net was outperformed existing SOTA models in industrial defect detection tasks, significantly improving detection accuracy for multi-class defects in complex scenarios, reducing reliance on manual inspection, and effectively lowering scrap losses caused by defects, thus providing a reliable solution for intelligent quality inspection in the electronics manufacturing industry.
Keyword ：
Accuracy Accuracy Computational modeling Computational modeling Convolution Convolution deep learning deep learning Defect detection Defect detection Feature extraction Feature extraction Industrial defect detection Industrial defect detection Inspection Inspection Printed circuits Printed circuits Production Production production automation production automation small object detection small object detection Training Training Transformers Transformers visual transformer visual transformer
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| GB/T 7714 | Wang, Zelu , Luo, Ming , Xie, Xinghe et al. MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection [J]. | IEEE ACCESS , 2025 , 13 : 122603-122612 . |
| MLA | Wang, Zelu et al. "MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection" . | IEEE ACCESS 13 (2025) : 122603-122612 . |
| APA | Wang, Zelu , Luo, Ming , Xie, Xinghe , Sun, Yue , Tian, Xinyu , Chen, Zhengxuan et al. MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection . | IEEE ACCESS , 2025 , 13 , 122603-122612 . |
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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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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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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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Despite advances in the use of the strategy of pre-training then fine-tuning in low-level vision tasks, the increasing size of models presents significant challenges for this paradigm, particularly in terms of training time and memory consumption. In addition, unsatisfactory results may occur when pre-trained single-image models are directly applied to a multi-image domain. In this paper, we propose an efficient method for transferring a pre-trained single-image super-resolution transformer network to the domain of stereo image super-resolution (SteISR) using a parameter-efficient fine-tuning approach. Specifically, the concept of stereo adapters and spatial adapters are introduced, which are incorporated into the pre-trained single-image super-resolution transformer network. Subsequently, only the inserted adapters are trained on stereo datasets. Compared with the classical full fine-tuning paradigm, our method can effectively reduce training time and memory consumption by 57% and 15%, respectively. Moreover, this method allows us to train only 4.8% of the original model parameters, achieving state-of-the-art performance on four commonly used SteISR benchmarks. This technology is expected to improve stereo image resolution in various fields such as medical imaging and autonomous driving, thereby indirectly enhancing the accuracy of depth estimation and object recognition tasks.
Keyword ：
Autonomous driving Autonomous driving Parameter-efficient fine-tuning Parameter-efficient fine-tuning Stereo image super-resolution Stereo image super-resolution Transfer learning Transfer learning
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| GB/T 7714 | Zhou, Yuanbo , Xue, Yuyang , Zhang, Xinlin et al. A universal parameter-efficient fine-tuning approach for stereo image super-resolution [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 151 . |
| MLA | Zhou, Yuanbo et al. "A universal parameter-efficient fine-tuning approach for stereo image super-resolution" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 151 (2025) . |
| APA | Zhou, Yuanbo , Xue, Yuyang , Zhang, Xinlin , Deng, Wei , Wang, Tao , Tan, Tao et al. A universal parameter-efficient fine-tuning approach for stereo image super-resolution . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 151 . |
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Background and objective: Modern deep neural networks are highly over-parameterized, necessitating the use of data augmentation techniques to prevent overfitting and enhance generalization. Generative adversarial networks (GANs) are popular for synthesizing visually realistic images. However, these synthetic images often lack diversity and may have ambiguous class labels. Recent data mixing strategies address some of these issues by mixing image labels based on salient regions. Since the main diagnostic information is not always contained within the salient regions, we aim to address the resulting label mismatches in medical image classifications. Methods: We propose a variety-guided data mixing framework (VariMix), which exploits an absolute difference map (ADM) to address the label mismatch problems of mixed medical images. VariMix generates ADM using the image-to-image (I2I) GAN across multiple classes and allows for bidirectional mixing operations between the training samples. Results: The proposed VariMix achieves the highest accuracy of 99.30% and 94.60% with a SwinT V2 classifier on a Chest X-ray (CXR) dataset and a Retinal dataset, respectively. It also achieves the highest accuracy of 87.73%, 99.28%, 95.13%, and 95.81% with a ConvNeXt classifier on a Breast Ultrasound (US) dataset, a CXR dataset, a Retinal dataset, and a Maternal-Fetal US dataset, respectively. Furthermore, the medical expert evaluation on generated images shows the great potential of our proposed I2I GAN in improving the accuracy of medical image classifications. Conclusions: Extensive experiments demonstrate the superiority of VariMix compared with the existing GAN- and Mixup-based methods on four public datasets using Swin Transformer V2 and ConvNeXt architectures. Furthermore, by projecting the source image to the hyperplanes of the classifiers, the proposed I2I GAN can generate hyperplane difference maps between the source image and the hyperplane image, demonstrating its ability to interpret medical image classifications. The source code is provided in https://github.com/yXiangXiong/VariMix. © 2025 Elsevier B.V.
Keyword ：
Classification (of information) Classification (of information) Diagnosis Diagnosis Image classification Image classification Image enhancement Image enhancement Medical image processing Medical image processing Medical problems Medical problems Mixing Mixing
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| GB/T 7714 | Xiong, Xiangyu , Sun, Yue , Liu, Xiaohong et al. VariMix: A variety-guided data mixing framework for explainable medical image classifications [J]. | Computer Methods and Programs in Biomedicine , 2025 , 271 . |
| MLA | Xiong, Xiangyu et al. "VariMix: A variety-guided data mixing framework for explainable medical image classifications" . | Computer Methods and Programs in Biomedicine 271 (2025) . |
| APA | Xiong, Xiangyu , Sun, Yue , Liu, Xiaohong , Ke, Wei , Lam, Chan-Tong , Gao, Qinquan et al. VariMix: A variety-guided data mixing framework for explainable medical image classifications . | Computer Methods and Programs in Biomedicine , 2025 , 271 . |
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