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.

翻译：在大规模图像中分割细小稀疏结构时，体素级、网格束缚的计算和极端类别不平衡构成了根本性制约——稠密全分辨率推理可扩展性差，迫使大多数流水线依赖固定区域化或下采样，将计算成本与图像分辨率绑定，并在少数类结构信息最丰富处削弱边界证据。我们提出SEMIR（语义子图诱导表示学习），一种通过学习任务自适应、保持拓扑结构的潜在图表示并实现精确解码来将推理从原生网格解耦的表示框架。SEMIR通过参数化边收缩、节点删除和边删除，将底层网格图转换为紧凑的边界对齐子图，同时保留从子图预测到晶格标签的精确提升映射。子图构建被形式化为一个少样本结构学习问题，用边界对齐目标替代手工调参的预处理：通过边界Dice准则最大化预测边界元素与目标特定语义边界的对齐度来学习子图参数；诱导子图附带尺度与旋转鲁棒的几何及强度描述符，并支持通过带关系边特征的图神经网络（GNN）进行高效的区域级信息传递。我们在三个肿瘤分割数据集（BraTS 2021、KiTS23和LiTS）上对SEMIR进行基准测试，这些数据集中的目标表现出高结构变异性和分布不确定性。SEMIR在实际运行时间内对少数类结构Dice系数取得了一致改进。更广泛而言，SEMIR建立了一个框架，用于学习任务自适应、保持拓扑结构的潜在表示，并通过精确解码处理高分辨率结构化视觉数据。