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学者姓名：童同
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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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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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Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical image segmentation has witnessed escalating popularity. Nevertheless, the intricate nature of medical image poses challenges on the segmentation of diminutive targets is still in its early stages. Current networks encounter difficulties in addressing the segmentation of exceedingly small targets, especially when the number of training samples is limited. To overcome this constraint, we have implemented a proficient strategy to enhance lesion images containing small targets and constrained samples. We introduce a segmentation framework termed STS-Net, specifically designed for small target segmentation. This framework leverages the established capacity of convolutional neural networks to acquire effective image representations. The proposed STS-Net network adopts a ResNeXt50-32x4d architecture as the encoder, integrating attention mechanisms during the encoding phase to amplify the feature representation capabilities of the network. We evaluated the proposed network on four publicly available datasets. Experimental results underscore the superiority of our approach in the domain of medical image segmentation, particularly for small target segmentation. The codes are available at https://github.com/zlxokok/STSNet.
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| GB/T 7714 | Zhao, Longxuan , Wang, Tao , Chen, Yuanbin et al. A novel framework for segmentation of small targets in medical images [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Zhao, Longxuan et al. "A novel framework for segmentation of small targets in medical images" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Zhao, Longxuan , Wang, Tao , Chen, Yuanbin , Zhang, Xinlin , Tang, Hui , Lin, Fuxin et al. A novel framework for segmentation of small targets in medical images . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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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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In recent years, supervised learning using convolutional neural networks (CNN) has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Moreover, effective utilization of annotation resources might not always be feasible during the annotation process. To optimize the utilization of annotation resources, a proposed active learning framework is introduced that is applicable to both 2D and 3D segmentation and classification tasks. This framework aims to reduce annotation costs by selecting more valuable samples for annotation from the pool of unlabeled data. Based on the perturbation consistency, we apply different perturbations to the input data and propose a perturbation consistency evaluation module to evaluate the consistency among predictions when applying different perturbations to the data. Subsequently, we rank the consistency of each data and select samples with lower consistency as high-value candidates. These selected samples are prioritized for annotation. We extensively validated our proposed framework on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our proposed framework can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The proposed framework enables more efficient utilization of annotation resources by annotating more representative samples, thus enhancing the model's robustness with fewer annotation costs.
Keyword ：
active learning active learning Active learning Active learning Annotations Annotations Data models Data models Image classification Image classification Image segmentation Image segmentation Machine learning Machine learning Medical diagnostic imaging Medical diagnostic imaging medical image classification medical image classification medical image segmentation medical image segmentation perturbation consistency perturbation consistency Perturbation methods Perturbation methods Supervised learning Supervised learning Three-dimensional displays Three-dimensional displays Training Training
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| GB/T 7714 | Wang, Tao , Zhang, Xinlin , Zhou, Yuanbo et al. PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2025 , 9 (4) : 3162-3177 . |
| MLA | Wang, Tao et al. "PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 9 . 4 (2025) : 3162-3177 . |
| APA | Wang, Tao , Zhang, Xinlin , Zhou, Yuanbo , Chen, Yuanbin , Zhao, Longxuan , Tan, Tao et al. PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2025 , 9 (4) , 3162-3177 . |
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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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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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