Differentiable fuzzy logic constraints fine-tune SAM to generate higher-quality pseudo-labels, enabling a second-stage model to reach state-of-the-art weakly supervised segmentation on Pascal VOC and REFUGE2, sometimes beating dense supervision.
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SAM-family models split into occluder-aware types that avoid predicting into occluded regions and occluder-agnostic types that confidently segment hidden areas, shown via a new benchmark on polyp datasets.
Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
DC-TTA improves interactive segmentation accuracy by partitioning user clicks into subsets for independent test-time adaptation of SAM models and merging the specialized predictors.
Adapting image editing foundation models via LoRA with multi-reference conditioning achieves state-of-the-art CT metal artifact reduction using two orders of magnitude less paired training data than prior methods.
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.
AHCQ-SAM introduces ACNR, HLUQ, CAG, and LNQ quantization techniques that deliver 15.2% mAP gain on 4-bit SAM-B and 14.01% J&F gain on 4-bit SAM2-Tiny versus prior PTQ methods.
RoiMAM integrates a training-free ROI Generation Module with Semantic Selective Suppression and a Text Prompt Enhancer to produce a compact VLM that reports 2 percent and 4.6 percent accuracy gains on SLAKE and PMC-VQA at less than 20 percent the size of MedVInT-TD.
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
Semi-supervised fetal cardiac ultrasound analysis using SAM-Med2D boundary refinement and DINOv3 semantic enhancement on the EchoCare backbone reports 79.99% Dice, 61.62% NSD, and 41.20% F1 on the FETUS 2026 leaderboard.
MAE-SAM2 integrates MAE self-supervised learning with SAM2 to achieve superior segmentation of retinal vascular leakage on fluorescein angiography images, with highest Dice/IoU scores and 5% improvement over original SAM2.
citing papers explorer
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Weakly Supervised Segmentation as Semantic-Based Regularization
Differentiable fuzzy logic constraints fine-tune SAM to generate higher-quality pseudo-labels, enabling a second-stage model to reach state-of-the-art weakly supervised segmentation on Pascal VOC and REFUGE2, sometimes beating dense supervision.
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Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
SAM-family models split into occluder-aware types that avoid predicting into occluded regions and occluder-agnostic types that confidently segment hidden areas, shown via a new benchmark on polyp datasets.
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Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy
Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
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DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
DC-TTA improves interactive segmentation accuracy by partitioning user clicks into subsets for independent test-time adaptation of SAM models and merging the specialized predictors.
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Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
Adapting image editing foundation models via LoRA with multi-reference conditioning achieves state-of-the-art CT metal artifact reduction using two orders of magnitude less paired training data than prior methods.
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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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AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization
AHCQ-SAM introduces ACNR, HLUQ, CAG, and LNQ quantization techniques that deliver 15.2% mAP gain on 4-bit SAM-B and 14.01% J&F gain on 4-bit SAM2-Tiny versus prior PTQ methods.
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RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding
RoiMAM integrates a training-free ROI Generation Module with Semantic Selective Suppression and a Text Prompt Enhancer to produce a compact VLM that reports 2 percent and 4.6 percent accuracy gains on SLAKE and PMC-VQA at less than 20 percent the size of MedVInT-TD.
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Weight Group-wise Post-Training Quantization for Medical Foundation Model
Permutation-COMQ is a new post-training quantization algorithm that reorders weights within layers and uses only dot-product and rounding steps to deliver the highest reported accuracy for 2-, 4-, and 8-bit medical foundation models.
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Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement
Semi-supervised fetal cardiac ultrasound analysis using SAM-Med2D boundary refinement and DINOv3 semantic enhancement on the EchoCare backbone reports 79.99% Dice, 61.62% NSD, and 41.20% F1 on the FETUS 2026 leaderboard.
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MAE-SAM2: Mask Autoencoder-Enhanced SAM2 for Clinical Retinal Vascular Leakage Segmentation
MAE-SAM2 integrates MAE self-supervised learning with SAM2 to achieve superior segmentation of retinal vascular leakage on fluorescein angiography images, with highest Dice/IoU scores and 5% improvement over original SAM2.
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