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‎index.html‎

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<article class="publication">
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<a class="paper-visual-link" href="https://arxiv.org/abs/2607.25393" target="_blank" rel="noreferrer" aria-label="View the DMCoStain paper">
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<span class="paper-badge">ACM MM 2026</span>
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<img class="paper-thumbnail" src="images/publications/dmco-framework.png" alt="Overview of the DMCoStain framework" loading="lazy" />
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</a>
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<div class="paper-copy">
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Siyuan Xu, Yan Wang, <strong>Haofei Song</strong>, Lili Gao, Jiansheng Wang, Qing Zhang,
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Dan Huang, Boxiang Yun, Hongkai Xiong, Qingli Li
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</p>
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<p class="venue"><em>ACM Multimedia (ACM MM)</em>, 2026</p>
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<p class="paper-links">
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<a href="https://arxiv.org/abs/2607.25393" target="_blank" rel="noreferrer">paper</a>
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<span>/</span>
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<a href="https://github.com/SikangSHU/DMCoStain" target="_blank" rel="noreferrer">code</a>
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</p>
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<!-- <p class="paper-summary">A data-model co-optimization framework for reliable and interpretable virtual IHC staining.</p> -->
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<p class="paper-summary">An iterative data-model co-optimization framework for reliable and interpretable virtual IHC staining.</p>
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</div>
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</article>
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<article class="publication featured">
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<article class="publication">
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<a class="paper-visual-link" href="https://arxiv.org/abs/2606.26716" target="_blank" rel="noreferrer" aria-label="View the DP-NSL paper">
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<span class="paper-badge">ECCV 2026</span>
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<img class="paper-thumbnail" src="images/publications/dpnsl-framework.png" alt="Overview of the DP-NSL framework" loading="lazy" />
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</a>
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<div class="paper-copy">
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<a class="paper-title" href="https://arxiv.org/abs/2606.26716" target="_blank" rel="noreferrer">
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Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution
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</a>
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<p class="authors"><strong>Haofei Song</strong>, Siyuan Xu, Xintian Mao, Shaojie Guo, Qingli Li, Yan Wang</p>
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<p class="venue"><em>European Conference on Computer Vision (ECCV)</em>, 2026</p>
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<p class="paper-links">
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<a href="https://arxiv.org/abs/2606.26716" target="_blank" rel="noreferrer">paper</a>
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<span>/</span>
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<a href="https://github.com/DeepMed-Lab-ECNU/Medical-Image-Reconstruction" target="_blank" rel="noreferrer">code</a>
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</p>
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<!-- <p class="paper-summary">Measurement-consistent arbitrary-scale medical slice reconstruction with geometry-aware spline priors.</p> -->
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<p class="paper-summary">Measurement-consistent arbitrary-scale medical slice reconstruction with geometry-aware spline priors.</p>
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</div>
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</article>
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<article class="publication">
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<a class="paper-visual-link" href="https://arxiv.org/abs/2605.23282" target="_blank" rel="noreferrer" aria-label="View the DGNO paper">
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<span class="paper-badge">ICML 2026</span>
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<img class="paper-thumbnail" src="images/publications/dgno-framework.png" alt="Architecture of the DGNO pathology deblurring method" loading="lazy" />
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</a>
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<div class="paper-copy">
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<a class="paper-title" href="https://arxiv.org/abs/2605.23282" target="_blank" rel="noreferrer">
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Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring
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</a>
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<p class="authors">Shaoqing Duan, <strong>Haofei Song</strong>, Xintian Mao, Qingli Li, Yan Wang</p>
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<p class="venue"><em>International Conference on Machine Learning (ICML)</em>, 2026</p>
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<p class="paper-links">
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<a href="https://arxiv.org/abs/2605.23282" target="_blank" rel="noreferrer">paper</a>
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<span>/</span>
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<a href="https://github.com/DeepMed-Lab-ECNU/Single-Image-Deblur" target="_blank" rel="noreferrer">code</a>
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</p>
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<!-- <p class="paper-summary">A neural operator formulation for spatially varying and locally discontinuous pathology blur.</p> -->
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<p class="paper-summary">A neural operator designed to model spatially varying and locally discontinuous blur in pathology images.</p>
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</div>
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</article>
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<article class="publication">
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<a class="paper-visual-link" href="https://arxiv.org/abs/2511.21132" target="_blank" rel="noreferrer" aria-label="View the DeepRFTv2 paper">
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<span class="paper-badge">arXiv 2025</span>
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<img class="paper-thumbnail" src="images/publications/deeprftv2-framework.png" alt="Architecture and building blocks of DeepRFTv2" loading="lazy" />
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</a>
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<a class="paper-title" href="https://arxiv.org/abs/2511.21132" target="_blank" rel="noreferrer">
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DeepRFTv2: Kernel-Level Learning for Image Deblurring
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</a>
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<p class="authors">Xintian Mao, <strong>Haofei Song</strong>, Yin-Nian Liu, Qingli Li, Yan Wang</p>
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<p class="venue"><em>arXiv preprint</em>, 2025</p>
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<p class="paper-links">
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<a href="https://arxiv.org/abs/2511.21132" target="_blank" rel="noreferrer">paper</a>
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<span>/</span>
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<a href="https://github.com/DeepMed-Lab-ECNU/Single-Image-Deblur" target="_blank" rel="noreferrer">code</a>
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</p>
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<!-- <p class="paper-summary">Fourier kernel estimation enables a deblurring network to learn the blur process at kernel level.</p> -->
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<p class="paper-summary">A frequency-domain deblurring network that learns the image degradation process at the kernel level.</p>
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</div>
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</article>
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<a class="paper-visual-link" href="https://www.ijcai.org/proceedings/2025/236" target="_blank" rel="noreferrer" aria-label="View the ATST-Net paper">
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<span class="paper-badge">IJCAI 2025</span>
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<img class="paper-thumbnail" src="images/publications/atst-framework.png" alt="Overview of the ATST-Net framework" loading="lazy" />
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<a class="paper-title" href="https://www.ijcai.org/proceedings/2025/236" target="_blank" rel="noreferrer">
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Advancing Stain Transfer for Multi-Biomarkers: A Human Annotation-Free Method Based on Auxiliary Task Supervision
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</a>
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<p class="authors">Siyuan Xu, <strong>Haofei Song</strong>, Yingjiao Deng, Jiansheng Wang, Yan Wang, Qingli Li</p>
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<p class="venue"><em>International Joint Conference on Artificial Intelligence (IJCAI)</em>, 2025</p>
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<p class="paper-links">
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<a href="https://www.ijcai.org/proceedings/2025/236" target="_blank" rel="noreferrer">paper</a>
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<span>/</span>
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<a href="https://github.com/SikangSHU/ATST-Net" target="_blank" rel="noreferrer">code</a>
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<!-- <p class="paper-summary">Human annotation-free auxiliary supervision for pathologically consistent virtual staining.</p> -->
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<p class="paper-summary">A human annotation-free framework for pathologically consistent multi-biomarker stain transfer.</p>
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</div>
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</article>
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<article class="publication">
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<a class="paper-visual-link" href="https://doi.org/10.1145/3664647.3681441" target="_blank" rel="noreferrer" aria-label="View the GeNSeg-Net paper">
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<span class="paper-badge">ACM MM 2024</span>
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<img class="paper-thumbnail" src="images/publications/genseg-framework.png" alt="Overview of the GeNSeg-Net framework" loading="lazy" />
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</a>
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<div class="paper-copy">
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<a class="paper-title" href="https://doi.org/10.1145/3664647.3681441" target="_blank" rel="noreferrer">
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GeNSeg-Net: A General Segmentation Framework for Any Nucleus in Immunohistochemistry Images
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</a>
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<p class="authors">Siyuan Xu, Guannan Li, <strong>Haofei Song</strong>, Jiansheng Wang, Yan Wang, Qingli Li</p>
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<p class="venue"><em>ACM Multimedia (ACM MM)</em>, 2024</p>
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<p class="paper-links"><a href="https://doi.org/10.1145/3664647.3681441" target="_blank" rel="noreferrer">paper</a></p>
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<!-- <p class="paper-summary">A general framework for segmenting diverse nuclei in immunohistochemistry images.</p> -->
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<p class="paper-summary">A general segmentation framework for diverse nuclei in immunohistochemistry images.</p>
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</div>
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<a class="paper-visual-link" href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/3655_ECCV_2024_paper.php" target="_blank" rel="noreferrer" aria-label="View the SVDSR paper">
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<span class="paper-badge">ECCV 2024</span>
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<img class="paper-thumbnail" src="images/publications/svdsr-framework.png" alt="Framework of the SVDSR degradation model" loading="lazy" />
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</a>
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<a class="paper-title" href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/3655_ECCV_2024_paper.php" target="_blank" rel="noreferrer">
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Spatially-Variant Degradation Model for Dataset-Free Super-Resolution
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</a>
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<p class="authors">Shaojie Guo, <strong>Haofei Song</strong>, Qingli Li, Yan Wang</p>
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<p class="venue"><em>European Conference on Computer Vision (ECCV)</em>, 2024</p>
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<a href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/html/3655_ECCV_2024_paper.php" target="_blank" rel="noreferrer">paper</a>
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<a href="https://github.com/DeepMed-Lab-ECNU/SVDSR" target="_blank" rel="noreferrer">code</a>
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<!-- <p class="paper-summary">Pixel-wise spatially varying degradation modeling for dataset-free blind super-resolution.</p> -->
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<p class="paper-summary">A pixel-wise spatially varying degradation model for dataset-free blind super-resolution.</p>
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<a class="paper-visual-link" href="https://arxiv.org/abs/2405.02857" target="_blank" rel="noreferrer" aria-label="View the I3Net paper">
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<span class="paper-badge">IEEE TMI 2024</span>
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<img class="paper-thumbnail" src="images/publications/i3net-framework.png.png" alt="Architecture and main blocks of the I3Net medical slice synthesis network" loading="lazy" />
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<a class="paper-title" href="https://arxiv.org/abs/2405.02857" target="_blank" rel="noreferrer">
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I³Net: Inter-Intra-Slice Interpolation Network for Medical Slice Synthesis
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</a>
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<p class="authors"><strong>Haofei Song</strong>, Xintian Mao, Jing Yu, Qingli Li, Yan Wang</p>
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<p class="venue"><em>IEEE Transactions on Medical Imaging (TMI)</em>, 2024</p>
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<a href="https://arxiv.org/abs/2405.02857" target="_blank" rel="noreferrer">paper</a>
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<a href="https://github.com/eeeric-code/I3Net" target="_blank" rel="noreferrer">code</a>
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<!-- <p class="paper-summary">Cross-view learning that combines through-plane and in-plane information for CT and MR slice synthesis.</p> -->
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<p class="paper-summary">A cross-view network combining inter-slice and intra-slice information for medical slice synthesis.</p>
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</article>
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</section>

‎stylesheet.css‎

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