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.
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13 Pith papers cite this work, alongside 32 external citations. Polarity classification is still indexing.
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UNVERDICTED 13representative citing papers
A multimodal 3D foundation model pretrained on LSM volumes via masked reconstruction and image-text alignment enables improved few-shot segmentation, classification, and deblurring.
MedFlowSeg is a conditional flow matching model for medical image segmentation that adds dual-branch spatial attention and frequency-aware attention to achieve more efficient inference than diffusion models while improving structural consistency.
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
Diff-UMamba combines UNet with Mamba and adds signal differencing for noise reduction, yielding 1-3% segmentation gains on public medical datasets and 4-5% on a small internal lung cancer dataset under limited data conditions.
A semi-supervised VAE combined with static and residual motion LDMs generates anatomically consistent 4D cardiac MRI, achieving Pearson r > 0.8 controllability and 1.4% Dice improvement in downstream segmentation when used for data augmentation.
MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
SGP-SAM transfers 3D SAM to lesion segmentation using a self-gated module for conditional multi-scale enhancement and a Zoom Loss, achieving 7.3% mDice gain over fine-tuning on MSD Liver Tumor data.
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with AUCs of 88.2%, 79.2%, and 78.1% on an internal cohort of 397 patients.
The HECKTOR 2025 challenge establishes performance benchmarks for multimodal PET/CT analysis of head and neck cancer, with top methods reaching Dice 0.75 on tumor segmentation, C-index 0.66 on recurrence-free survival, and balanced accuracy 0.56 on HPV classification.
citing papers explorer
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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.
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A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring
A multimodal 3D foundation model pretrained on LSM volumes via masked reconstruction and image-text alignment enables improved few-shot segmentation, classification, and deblurring.
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MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention
MedFlowSeg is a conditional flow matching model for medical image segmentation that adds dual-branch spatial attention and frequency-aware attention to achieve more efficient inference than diffusion models while improving structural consistency.
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Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI
CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.
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Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios
Diff-UMamba combines UNet with Mamba and adds signal differencing for noise reduction, yielding 1-3% segmentation gains on public medical datasets and 4-5% on a small internal lung cancer dataset under limited data conditions.
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Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis
A semi-supervised VAE combined with static and residual motion LDMs generates anatomically consistent 4D cardiac MRI, achieving Pearson r > 0.8 controllability and 1.4% Dice improvement in downstream segmentation when used for data augmentation.
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MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.
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Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging
A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classification tasks.
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Align then Refine: Text-Guided 3D Prostate Lesion Segmentation
A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.
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SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation
SGP-SAM transfers 3D SAM to lesion segmentation using a self-gated module for conditional multi-scale enhancement and a Zoom Loss, achieving 7.3% mDice gain over fine-tuning on MSD Liver Tumor data.
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Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
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AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer
An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with AUCs of 88.2%, 79.2%, and 78.1% on an internal cohort of 397 patients.
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HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
The HECKTOR 2025 challenge establishes performance benchmarks for multimodal PET/CT analysis of head and neck cancer, with top methods reaching Dice 0.75 on tumor segmentation, C-index 0.66 on recurrence-free survival, and balanced accuracy 0.56 on HPV classification.