SEMIR replaces dense voxel computation with a learned topology-preserving graph minor that supports exact decoding and GNN-based inference for small-structure segmentation in large medical images.
Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Dynamic Focal Attention learns class-specific difficulty via per-class biases in attention logits, improving Dice and IoU on imbalanced histopathology segmentation benchmarks.
citing papers explorer
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SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
SEMIR replaces dense voxel computation with a learned topology-preserving graph minor that supports exact decoding and GNN-based inference for small-structure segmentation in large medical images.
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Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention
Dynamic Focal Attention learns class-specific difficulty via per-class biases in attention logits, improving Dice and IoU on imbalanced histopathology segmentation benchmarks.