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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Nature Communications15(1), 654 (1 2024)
14 Pith papers cite this work, alongside 2,233 external citations. Polarity classification is still indexing.
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Neuro-JEPA, a JEPA-and-MoE transformer pretrained on 1.55M brain MRI scans, outperformed prior neuroimaging foundation models and was the only one to beat a simple CNN baseline on average.
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
SAMRI fine-tunes only the mask decoder of SAM on 1.1 million MRI slices from 30 datasets to reach mean DSC 0.87 on 47 targets and strong zero-shot performance.
SemanticVessel is a new CTA vessel segmentation dataset using intensity-guided region growing, expert labeling of 20 arterial classes, and multi-phase label reuse, with reported gains from including a generic minor-artery class.
ViTC-UNet adapts frozen ViT representations to biomedical semantic segmentation by conditioning a UNet via learnable tokens and two-way attention decoding.
A novel weakly supervised anomaly detection method for brain MRI that uses discriminative dual prompt tuning for pseudo masks and region-aware spatial attention with location-based random embeddings to achieve SOTA results with under 8 million parameters on BraTS and MSD datasets.
PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.
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.
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.
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.
FLIM-BoFP replaces per-block patch clustering in FLIM networks with a single input-level clustering step that creates a bag of feature points used to define filters across all encoder blocks, yielding faster training for parasite detection in optical microscopy.
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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Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging
Neuro-JEPA, a JEPA-and-MoE transformer pretrained on 1.55M brain MRI scans, outperformed prior neuroimaging foundation models and was the only one to beat a simple CNN baseline on average.
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MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
-
RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
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Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
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SAMRI: Segment Any MRI
SAMRI fine-tunes only the mask decoder of SAM on 1.1 million MRI slices from 30 datasets to reach mean DSC 0.87 on 47 targets and strong zero-shot performance.
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Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
SemanticVessel is a new CTA vessel segmentation dataset using intensity-guided region growing, expert labeling of 20 arterial classes, and multi-phase label reuse, with reported gains from including a generic minor-artery class.
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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.
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RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans
A novel weakly supervised anomaly detection method for brain MRI that uses discriminative dual prompt tuning for pseudo masks and region-aware spatial attention with location-based random embeddings to achieve SOTA results with under 8 million parameters on BraTS and MSD datasets.
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PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.
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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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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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Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster convergence and higher Dice scores in cross-domain MRI tasks.
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FLIM Networks with Bag of Feature Points
FLIM-BoFP replaces per-block patch clustering in FLIM networks with a single input-level clustering step that creates a bag of feature points used to define filters across all encoder blocks, yielding faster training for parasite detection in optical microscopy.