Dan He

DCFusion: Dynamic Continuous Fusion of Medical Images Based on Intensity-Varying Flow

Lijian Yang, Guofen Wang, Yuping Huang, Dan He et autres

Medical Image Fusion (MIF) integrates information from multiple imaging modalities to obtain comprehensive fused images essential for accurate diagnosis and treatment planning. Existing methods, based on deep learning or traditional machine learning, typically generate fused images or fusion weights directly. However, these …

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SAFusion: Scenario-Adaptive Network for Multimodal Medical Image Fusion

Weisheng Li, Pengtao Jia, Dan He, Siqi Liu et autres

Multimodal medical image fusion aims to integrate complementary information from different modalities to support clinical diagnosis and treatment. Although deep learning has significantly advanced this field, existing methods often overlook the differences between various fusion scenarios, making a single network inadequate for …

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LTOFusion: A Learning-to-Optimize Framework With Flow Matching for Unsupervised Image Fusion

Dan He, Lijian Yang, Guofen Wang, Yuping Huang et autres

Multimodal Image Fusion (MMIF) aims to synthesize complementary information from different modalities to generate comprehensive fused images, thereby facilitating downstream applications. Existing methods typically employ deep neural networks to directly construct high-dimensional image-to-image mappings, which is highly challenging, struggling to extract generalizable …

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Rethinking normalization strategies and convolutional kernels for multimodal image fusion

Dan He, Guofen Wang, Weisheng Li, Yucheng Shu et autres

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Research on a Non-Contact Cattle Weight Measurement System Based on Deep Learning

Lihao Qin, Zhongyu Ma, Zhanshuo Zhang, Yiping Zuo et autres

As the smart livestock industry continues to evolve, traditional methods of measuring cattle weight are increasingly inadequate for the demands of large-scale farms. Conventional approaches rely on manual weighing or bulky equipment, which are inefficient and lack precision, failing to provide real-time, …

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DM-FNet: Unified multimodal medical image fusion via diffusion process-trained encoder-decoder

Dan He, Weisheng Li, Guofen Wang, Yuping Huang et autres

Multimodal medical image fusion (MMIF) extracts the most meaningful information from multiple source images, enabling a more comprehensive and accurate diagnosis. Achieving high-quality fusion results requires a careful balance of brightness, color, contrast, and detail; this ensures that the fused images effectively …

Research on Deep Forged Image Detection Based on Deep Learning Fusion of Multidimensional Recognition Features

X. Chu, Dan He

This study relies on deep learning to develop a deep forgery image detection model that integrates multidimensional recognition features. The use of deep learning techniques to extract and explore visual and semantic features of forged images covers a comprehensive coverage from pixel …

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Contrastive Learning Guided Fusion Network for Brain CT and MRI

Yuping Huang, Weisheng Li, Bin Jie Xiao, Guofen Wang et autres

Medical image fusion technology provides professionals with more detailed and precise diagnostic information. This paper introduces a new efficient CT and MRI fusion network, CLGFusion, based on a contrastive learning-guided network. CLGFusion includes two encoding branches at the feature encoding stage, enabling …

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SSEFusion: Salient semantic enhancement for multimodal medical image fusion with Mamba and dynamic spiking neural networks

Shiqiang Liu, Weisheng Li, Dan He, Guofen Wang et autres

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DM-FNet: Unified Multimodal Medical Image Fusion via Diffusion Process-Trained Encoder-Decoder

Dan He, Weisheng Li, Guofen Wang, Yuping Huang et autres

Multimodal medical image fusion (MMIF) extracts the most meaningful information from multiple source images, enabling a more comprehensive and accurate diagnosis. Achieving high-quality fusion results requires a careful balance of brightness, color, contrast, and detail; this ensures that the fused images effectively …

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Rethinking Normalization Strategies and Convolutional Kernels for Multimodal Image Fusion

Dan He, Guofen Wang, Weisheng Li, Yucheng Shu et autres

Multimodal image fusion (MMIF) integrates information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing research focuses on complementary information fusion and training strategies, overlooking the critical role of underlying architectural components like normalization and convolution kernels. We …

A generative adversarial network based on deep supervision for anatomical and functional image fusion

Shiqiang Liu, Weisheng Li, Guofen Wang, Yuping Huang et autres

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