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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nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation
14 Pith papers cite this work. Polarity classification is still indexing.
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A Jacobian sensitivity curve computed at initialization identifies the narrowest U-Net configuration that avoids performance collapse, matching nnU-Net accuracy with 400-1600x fewer parameters on six medical datasets.
LARGO uses a low-rank hypernetwork with CP decomposition to unify 2^N-1 missing-modality models into one, ranking first in 47 of 52 configurations on BraTS and ISLES with small Dice gains over baselines.
MonoUNet is a tiny segmentation network that achieves 92-95% Dice scores on multi-device knee cartilage ultrasound while using 10-700x fewer parameters than prior lightweight models by injecting trainable local phase features.
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
Episodic sampling for class-balanced batches in low-data CT segmentation delays overfitting compared to random or weighted sampling, revealing training iteration budget as a key evaluation confound.
TwinTrack applies post-hoc calibration to align deep learning segmentation probabilities with the empirical mean human response for explicit modeling of inter-rater disagreement.
MIGF improves multi-modal prostate MRI segmentation robustness via modality-isolated streams and dropout training, yielding ranking score gains of 2.8-13.4% across backbones and better tolerance to degraded diffusion sequences on PI-CAI and Prostate158.
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.
SegSTRONG-C provides a new benchmark where top models reach 0.9394 DSC and 0.9301 NSD on corrupted surgical tool segmentation tests, showing conventional techniques help but calling for more innovative robustness methods.
Automated reference-region normalization of optical density in myelin histology yields substantially stronger voxel-wise correlation with 7T ex vivo MRI than unnormalized measurements, including inside white matter hyperintensities.
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.
DALight-3D achieves a mean Dice of 0.727 with 2.22M parameters on the Medical Segmentation Decathlon BrainTumour benchmark, slightly above the 0.710 Dice of Residual 3D U-Net with 3.20M parameters.
citing papers explorer
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XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity
A Jacobian sensitivity curve computed at initialization identifies the narrowest U-Net configuration that avoids performance collapse, matching nnU-Net accuracy with 400-1600x fewer parameters on six medical datasets.
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LARGO: Low-Rank Hypernetwork for Handling Missing Modalities
LARGO uses a low-rank hypernetwork with CP decomposition to unify 2^N-1 missing-modality models into one, ranking first in 47 of 52 configurations on BraTS and ISLES with small Dice gains over baselines.
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MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
MonoUNet is a tiny segmentation network that achieves 92-95% Dice scores on multi-device knee cartilage ultrasound while using 10-700x fewer parameters than prior lightweight models by injecting trainable local phase features.
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Fairboard: a quantitative framework for equity assessment of healthcare models
Patient identity and clinical features predict brain tumor segmentation accuracy more strongly than model choice, with localized spatial biases consistent across models and no formal fairness guarantees in any.
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TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
TwinTrack applies post-hoc calibration to align deep learning segmentation probabilities with the empirical mean human response for explicit modeling of inter-rater disagreement.
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Architecture-Agnostic Modality-Isolated Gated Fusion for Robust Multi-Modal Prostate MRI Segmentation
MIGF improves multi-modal prostate MRI segmentation robustness via modality-isolated streams and dropout training, yielding ranking score gains of 2.8-13.4% across backbones and better tolerance to degraded diffusion sequences on PI-CAI and Prostate158.
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Automated Optical Density Normalization for Myelin Quantification: Cross-Modal Validation with 7T Ex Vivo MRI
Automated reference-region normalization of optical density in myelin histology yields substantially stronger voxel-wise correlation with 7T ex vivo MRI than unnormalized measurements, including inside white matter hyperintensities.
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Few-Shot Left Atrial Wall Segmentation in 3D LGE MRI via Meta-Learning
MAML with auxiliary cavity tasks and boundary loss improves 5-shot LA wall segmentation over standard fine-tuning (DSC 0.54 vs 0.48) and nears fully supervised performance at 20 shots.
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Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.
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DALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI
DALight-3D achieves a mean Dice of 0.727 with 2.22M parameters on the Medical Segmentation Decathlon BrainTumour benchmark, slightly above the 0.710 Dice of Residual 3D U-Net with 3.20M parameters.