PR-MaGIC refines prompts in in-context segmentation via test-time gradient flow from the mask decoder plus top-1 selection, yielding better masks across benchmarks without training.
Medical SAM adapter: Adapting seg- ment anything model for medical image segmentation
9 Pith papers cite this work. Polarity classification is still indexing.
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SAMamba3D adapts a frozen SAM encoder with Mamba volumetric context and cross-scale features to match or exceed 3D baselines on diverse sandstone and carbonate datasets while reducing case-specific retraining.
SGPer combines DINOv2 semantic priors converted to dense prompts with SAM geometric priors through disease-sensitive adapters and dynamic consistency filtering to deliver robust limited-data wheat disease segmentation.
Presents COMMA, a coordinate-aware Mamba network for 3D vessel segmentation that uses global and local branches, along with a new 570-case labeled dataset.
SAM 2 delivers more accurate video segmentation with 3x fewer user interactions and 6x faster image segmentation than the original SAM by training a streaming-memory transformer on the largest video segmentation dataset collected to date.
DiffKT3D transfers priors from video diffusion models to 3D radiotherapy dose prediction via modality-specific embeddings and clinically guided RL, reducing voxel MAE from 2.07 to 1.93 and claiming SOTA over the GDP-HMM challenge winner.
DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 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.
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
citing papers explorer
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PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation
PR-MaGIC refines prompts in in-context segmentation via test-time gradient flow from the mask decoder plus top-1 selection, yielding better masks across benchmarks without training.
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SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images
SAMamba3D adapts a frozen SAM encoder with Mamba volumetric context and cross-scale features to match or exceed 3D baselines on diverse sandstone and carbonate datasets while reducing case-specific retraining.
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Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation
SGPer combines DINOv2 semantic priors converted to dense prompts with SAM geometric priors through disease-sensitive adapters and dynamic consistency filtering to deliver robust limited-data wheat disease segmentation.
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COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation
Presents COMMA, a coordinate-aware Mamba network for 3D vessel segmentation that uses global and local branches, along with a new 570-case labeled dataset.
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SAM 2: Segment Anything in Images and Videos
SAM 2 delivers more accurate video segmentation with 3x fewer user interactions and 6x faster image segmentation than the original SAM by training a streaming-memory transformer on the largest video segmentation dataset collected to date.
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Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
DiffKT3D transfers priors from video diffusion models to 3D radiotherapy dose prediction via modality-specific embeddings and clinically guided RL, reducing voxel MAE from 2.07 to 1.93 and claiming SOTA over the GDP-HMM challenge winner.
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Deep Reprogramming Distillation for Medical Foundation Models
DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 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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Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.