https://vilab.team/author/%E5%90%B4%E5%B0%8F%E6%BB%A1/ 吴小满 Hugo Blox Builder (https://hugoblox.com)en-usSun, 03 May 2026 00:00:00 +0000 https://vilab.team/author/%E5%90%B4%E5%B0%8F%E6%BB%A1/avatar_hu11397519880875122105.jpg https://vilab.team/author/%E5%90%B4%E5%B0%8F%E6%BB%A1/ https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4-2-%E9%A1%B9%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8/ Sun, 03 May 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4-2-%E9%A1%B9%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8/ <p>热烈祝贺陈政同学、吴小满同学！近期，实验室共有 2 项科研成果正式发表。</p> <h2 id="salient-diagnostic-value-perception-for-preoperative-posterior-fossa-tumor-diagnosis">Salient Diagnostic Value Perception For Preoperative Posterior Fossa Tumor Diagnosis</h2> <p><strong>祝贺陈政同学！该论文已发表在 <em>ICASSP 2026</em>。</strong></p> <ul> <li><strong>作者：</strong> Zheng Chen、Peng Li、Zheyu Zhang、Siying Wu、Jing Zhang、Cuiling Hu、Yunwei Ou、Xiaoyan Sun</li> <li><strong>发表载体：</strong> <em>ICASSP</em></li> <li><strong>发表时间：</strong> 2026年5月3日</li> <li><strong>相关链接：</strong> <a href="https://ieeexplore.ieee.org/abstract/document/11460465/" target="_blank" rel="noopener">论文链接</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出显著诊断价值感知方法（SDVP），用于后颅窝肿瘤的术前准确诊断。该方法整合MRI影像与放射学报告，从三个互补视角学习关键诊断线索：通过对抗性样本内对比学习增强跨中心与设备差异的鲁棒性；借助知识增强的样本内对比学习提取专家引导的样本特异性特征；并在干净MRI样本上进行监督式类间对比学习以强化类别特征。</p> <hr> <h2 id="sscm-a-spatial-semantic-consistent-model-for-multi-contrast-mri-super-resolution">SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution</h2> <p><strong>祝贺吴小满同学！该论文已发表在 <em>ICASSP 2026</em>。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4-2-%E9%A1%B9%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8/images/paper-02_hu16643979777475813095.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4-2-%E9%A1%B9%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8/images/paper-02_hu13958872581769715241.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4-2-%E9%A1%B9%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8/images/paper-02_hu13117256722096246496.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4-2-%E9%A1%B9%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8/images/paper-02_hu16643979777475813095.webp" width="760" height="438" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Xiaoman Wu、Lubin Gan、Siying Wu、Jing Zhang、Yunwei Ou、Xiaoyan Sun</li> <li><strong>发表载体：</strong> <em>ICASSP</em></li> <li><strong>发表时间：</strong> 2026年5月3日</li> <li><strong>相关链接：</strong> <a href="https://ieeexplore.ieee.org/abstract/document/11464902/" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2509.18593" target="_blank" rel="noopener">PDF</a></li> </ul> <h3 id="论文介绍-1">论文介绍</h3> <p>本文提出空间语义一致模型（SSCM），用于多对比度磁共振成像超分辨率。该方法通过动态空间扭曲模块实现对比度间空间对齐，利用语义感知令牌聚合块建模长程依赖，并结合空间-频率融合块恢复高频细节，从而在结构差异和运动干扰下保持解剖结构的空间语义一致性。实验表明SSCM在效率和性能上优于现有方法。</p>