做医学图像分割或处理极端类别不平衡问题的研究者，SEMIR 用图小类替代密集网格推理，既保持拓扑又降低计算量，值得关注其边界对齐的少样本学习思路。
针对大规模图像中微小稀疏结构分割的难题，SEMIR 提出了一种新的表示学习框架，将推理从原始像素网格解耦到拓扑保持的潜在图表示上。该方法通过参数化边收缩、节点和边删除，将网格图转化为紧凑、边界对齐的图小类，并保留从图预测到像素标签的精确映射。小类构建被形式化为少样本结构学习问题，通过边界 Dice 准则优化参数，使预测边界与目标语义边缘对齐。在 BraTS 2021、KiTS23 和 LiTS 三个肿瘤分割数据集上，SEMIR 在保持实用运行时间的同时，持续提升了小类结构的 Dice 系数。这项工作为高分辨率结构化视觉数据提供了一种学习任务自适应、拓扑保持潜在表示并支持精确解码的通用框架。
SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
Segmenting small and sparse structures in large-scale images is fundamentally constrained by voxel-level, lattice-bound computation and extreme class imbalance -- dense, full-resolution inference scales poorly and forces most pipelines to rely on fixed regionization or downsampling, coupling computational cost to image resolution and attenuating boundary evidence precisely where minority structures are most informative. We introduce SEMIR (Semantic Minor-Induced Representation Learning), a representation framework that decouples inference from the native grid by learning a task-adapted, topology-preserving latent graph representation with exact decoding. SEMIR transforms the underlying grid graph into a compact, boundary-aligned graph minor through parameterized edge contraction, node deletion, and edge deletion, while preserving an exact lifting map from minor predictions to lattice labels. Minor construction is formalized as a few-shot structure learning problem that replaces hand-tuned preprocessing with a boundary-alignment objective: minor parameters are learned by maximizing agreement between predicted boundary elements and target-specific semantic edges under a boundary Dice criterion, and the induced minor is annotated with scale- and rotation-robust geometric and intensity descriptors and supports efficient region-level inference via message passing on a graph neural network (GNN) with relational edge features. We benchmark SEMIR on three tumor segmentation datasets -- BraTS 2021, KiTS23, and LiTS -- where targets exhibit high structural variability and distributional uncertainty. SEMIR yields consistent improvements in minority-structure Dice at practical runtime. More broadly, SEMIR establishes a framework for learning task-adapted, topology-preserving latent representations with exact decoding for high-resolution structured visual data.