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.
In:2025IEEE22ndInternationalSymposiumonBiomedicalImaging(ISBI).pp.1– 4 (2025)
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
PhotIQA is a new public dataset of 1134 expert-rated photoacoustic images for benchmarking image quality assessment in medical imaging.
Fine-tuned MLLMs achieve competitive skeletal landmark localization on synthetic and real X-ray datasets compared to deep learning baselines and demonstrate reasoning for sequential C-arm navigation.
SMIT, which combines masked image modeling with self-distillation, delivers the highest segmentation accuracy, fastest convergence, and best few-shot performance across nine CT and MRI tasks compared to contrastive and rotation-based SSL methods.
ViTC-UNet adapts frozen ViT representations to biomedical semantic segmentation by conditioning a UNet via learnable tokens and two-way attention decoding.
MMAP uses multi-magnification patch features and slide-level prototype embeddings to predict spatial gene expression from H&E images and reports better MAE, MSE, and PCC than prior methods.
citing papers explorer
-
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.
-
PhotIQA: A photoacoustic image data set with image quality ratings
PhotIQA is a new public dataset of 1134 expert-rated photoacoustic images for benchmarking image quality assessment in medical imaging.
-
Autonomous Skeletal Landmark Localization towards Agentic C-Arm Control
Fine-tuned MLLMs achieve competitive skeletal landmark localization on synthetic and real X-ray datasets compared to deep learning baselines and demonstrate reasoning for sequential C-arm navigation.
-
Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks
SMIT, which combines masked image modeling with self-distillation, delivers the highest segmentation accuracy, fastest convergence, and best few-shot performance across nine CT and MRI tasks compared to contrastive and rotation-based SSL methods.
-
Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation
ViTC-UNet adapts frozen ViT representations to biomedical semantic segmentation by conditioning a UNet via learnable tokens and two-way attention decoding.
-
MMAP: A Multi-Magnification and Prototype-Aware Architecture for Predicting Spatial Gene Expression
MMAP uses multi-magnification patch features and slide-level prototype embeddings to predict spatial gene expression from H&E images and reports better MAE, MSE, and PCC than prior methods.
- When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI