Xiaoman Wu | ViLab https://vilab.team/author/xiaoman-wu/ Xiaoman Wu Hugo Blox Builder (https://hugoblox.com)en-usSun, 03 May 2026 00:00:00 +0000 https://vilab.team/media/icon_hu2896232876136423579.png Xiaoman Wu https://vilab.team/author/xiaoman-wu/ SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution https://vilab.team/publication/sscm-a-spatial-semantic-consistent-model-for-multi-contrast-/ Sun, 03 May 2026 00:00:00 +0000 https://vilab.team/publication/sscm-a-spatial-semantic-consistent-model-for-multi-contrast-/ <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> Semamil: Semantic reordering with retrieval-guided state space modeling for whole slide image classification https://vilab.team/publication/semamil-semantic-reordering-with-retrieval-guided-state-spac/ Fri, 01 Aug 2025 00:00:00 +0000 https://vilab.team/publication/semamil-semantic-reordering-with-retrieval-guided-state-spac/ <p>本文针对全切片图像分类中多实例学习忽略上下文、Transformer计算复杂、状态空间模型打乱语义顺序的问题，提出SemaMIL方法。该方法包含语义重排模块，通过可逆置换将语义相似的图像块聚类排列；以及语义引导检索状态空间模块，选择代表性查询子集调整状态空间参数，实现高效全局建模。在四个WSI数据集上验证了方法的有效性。</p>