TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct
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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.
MedSIGHT unifies medical image comprehension and segmentation in Med-LVLMs via a Region Perceiver module and region codebook, trained progressively on 72K pairs to reach SOTA on both tasks across modalities.
GenMed uses diffusion models to capture P(X,Y) for medical tasks and performs inference via gradient-based test-time optimization, supporting arbitrary observation combinations without retraining.
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.
A two-stage sparse convolutional network pipeline for native high-resolution 3D kidney and tumor segmentation in CT that matches top Dice scores while reducing VRAM and runtime versus nnU-Net and SegVol.
Introduces Implantable Adaptive Cells inserted into pre-trained U-Nets via Partially-Connected DARTS to achieve approximately 5 percentage point gains in segmentation accuracy on four medical MRI/CT datasets.
SAM (ViT-B) shows stable spleen segmentation in abdominal CT with mean Dice drop below 0.01 and no rise in failures under simulated domain shifts like noise, blur, and contrast changes.
Vision foundation models quantify aleatoric uncertainty via feature diversity and singular value energy to enable uncertainty-aware data filtering and dynamic training optimization for improved medical image segmentation.
A fine-tuned 3D foundation segmentation model combined with cross pseudo supervision achieves robust liver segmentation across labeled and unlabeled multi-phase, multi-vendor MRI without spatial registration.
citing papers explorer
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TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
TriALS introduces a 150-case four-phase CT dataset and challenge showing top segmentation methods reach 0.754 Dice on venous phase but only 0.57 on non-contrast CT, with external validation gains up to 28%.
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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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MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models
MedSIGHT unifies medical image comprehension and segmentation in Med-LVLMs via a Region Perceiver module and region codebook, trained progressively on 72K pairs to reach SOTA on both tasks across modalities.
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GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
GenMed uses diffusion models to capture P(X,Y) for medical tasks and performs inference via gradient-based test-time optimization, supporting arbitrary observation combinations without retraining.
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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.
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Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography
A two-stage sparse convolutional network pipeline for native high-resolution 3D kidney and tumor segmentation in CT that matches top Dice scores while reducing VRAM and runtime versus nnU-Net and SegVol.
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Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation
Introduces Implantable Adaptive Cells inserted into pre-trained U-Nets via Partially-Connected DARTS to achieve approximately 5 percentage point gains in segmentation accuracy on four medical MRI/CT datasets.
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Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment
SAM (ViT-B) shows stable spleen segmentation in abdominal CT with mean Dice drop below 0.01 and no rise in failures under simulated domain shifts like noise, blur, and contrast changes.
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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models
Vision foundation models quantify aleatoric uncertainty via feature diversity and singular value energy to enable uncertainty-aware data filtering and dynamic training optimization for improved medical image segmentation.
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Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
A fine-tuned 3D foundation segmentation model combined with cross pseudo supervision achieves robust liver segmentation across labeled and unlabeled multi-phase, multi-vendor MRI without spatial registration.