arXiv：cs.AI· Jialei He, Enhe Liu, Sifan Song, Pengfei Jin, Jionglong Su, Hongbin Wang, Zhixiang Lu, Yanhao Huang, Anteng Cai, Zhengyong Jiang, Jiaman Ding, S. Kevin Zhou, Jinfeng Wang·· 3 小时前
ContiLNN：用液态神经网络缓解切片采样不连续性的医学图像修复方法
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration
AI 导读
ContiLNN 通过双向闭式连续时间（Bi-CfC）模块为二维修复骨干网络加入跨切片建模，同时保留平面内特征提取。在 CT 去噪、MRI 超分辨率和低计数 PET 修复三项任务上，其平均 PSNR 较 Restore-RWKV 分别提升 0.1907、1.0176 和 1.2482 dB，RMSE 也均更低。
正文
Authors:Jialei He, Enhe Liu, Sifan Song, Pengfei Jin, Jionglong Su, Hongbin Wang, Zhixiang Lu, Yanhao Huang, Anteng Cai, Zhengyong Jiang, Jiaman Ding, S. Kevin Zhou, Jinfeng Wang
Abstract:Anatomical continuity provides complementary information for medical image restoration, but its use requires accounting for local anatomy and variations in slice sampling. We introduce ContiLNN, which augments two-dimensional restoration backbones with bidirectional closed-form continuous-time (Bi-CfC) modules for cross-slice modeling while retaining in-plane feature extraction. Slice-index intervals modulate gates determined by local features and hidden states, enabling propagation to respond to sampling variations without numerical ODE integration. Reference-guided consistency aligns first- and second-order cross-slice intensity differences to preserve anatomical variation, while distillation from a frozen backbone helps retain in-plane fidelity. Across five training seeds, ContiLNN improves mean PSNR over Restore-RWKV by 0.1907, 1.0176, and 1.2482 dB for CT denoising, MRI super-resolution, and reduced-count PET restoration, respectively, with lower RMSE in all three tasks. CT results are descriptive for one held-out patient. PET ablations support ordered propagation beyond additional pointwise capacity. Under contiguous training, Bi-CfC achieves higher fidelity than a Bi-GRU with similar parameter counts and arithmetic costs across all tested sampling conditions. Matched seven-slice profiling shows 52.8% lower latency and 57.0% lower peak GPU memory use than Bi-GRU. Mixed-gap training improves sparse and irregular-context performance for both operators, without a uniform ranking across metrics and contexts. Experiments with fewer training patients and a second backbone further support data efficiency and backbone compatibility.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12337 [cs.CV] |
| (or arXiv:2610.12337v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12337 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinfeng Wang [view email]
[v1]
Thu, 8 Oct 2026 17:13:32 UTC (16,083 KB)
来源：arXiv：cs.AI · arxiv.org