Papers Medical Image Segmentation
“Medical Image Segmentation” 태그가 달린 논문 2,546편 · 필터 해제
Uncertainty-Guided Handshake: Efficient Human-in-the-Loop Refinement for Surgical-Grade Glioma Segmentation
While state-of-the-art automated models for medical image segmentation achieve high mean performance, they frequently suffer from localized, catastrophic failures that preclude safe clinical deployment, particularly in n…
Medical Image SegmentationInteractive SegmentationLightMIS: Ultra-Lightweight Medical Image Segmentation Without a Stage-Wise Decoder
We present LightMIS, a scalable family of ultra-lightweight convolutional networks for 2D binary medical image segmentation without a learned stage-wise decoder. LightMIS aligns the outputs of a five-level encoder to a c…
Medical Image SegmentationDe-GAN - Dynamic Parameter Tuned GAN for 3D Medical Image Segmentation: A Step Towards Generalisation
Brain tumor segmentation remains difficult because enhancing tumor (ET) has low contrast and overlaps surrounding tissue, while scanner and site variation causes domain shift. We propose DE-GAN, a contrast-enhancing cond…
Medical Image SegmentationBrain Tumor SegmentationWhen Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentation
Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spatially aligned inputs differ in quality. Here, "corruption" primarily d…
Medical Image SegmentationFrom Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inference. This assumption is fragile when query…
Medical Image SegmentationInteractive SegmentationTest-time AdaptationLightMedSeg-ISLES: Stroke Lesion Segmentation with 81x Fewer Parameters than nnU-Net
Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present LightMedSeg-ISLES, a 1.26-million-parameter pipeline for T1-weighte…
Medical Image SegmentationLesion SegmentationCoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Multi-modal medical images and clinical reports provide complementary anatomical, functional, and semantic information for medical image segmentation. Effectively exploiting these heterogeneous sources requires fine-grai…
Medical Image SegmentationUnsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global anatomical structures, …
Medical Image SegmentationLiver SegmentationWhen Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning
Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot setups. However, their performance depends on the quality of prompts an…
Medical Image SegmentationVisual Prompt TuningLHMCF-Net: A Learned Hyperbolic Mean Curvature Flow Network for Medical Images Segmentation
Motivated by the classical Chan-Vese model and the ability of deep priors to capture complex spatial structures, we develop a segmentation model that leverages learned hyperbolic mean curvature flow (LHMCF) as a mathemat…
Medical Image SegmentationPrompt-Conditioned Channel Attention for Hierarchical Feature Modulation toward Anatomy-Agnostic Segmentation
Anatomically plausible segmentation remains challenging because of low contrast, ambiguous boundaries, and modality-specific artifacts. Interactive segmentation has emerged as a promising strategy to guide feature extrac…
Medical Image SegmentationInteractive SegmentationA Few Cases Are All You Need: An Empirical Study of Annotation-Efficient LoRA Fine-Tuning of MedSAM3
Medical image segmentation is essential for clinical workflows such as treatment planning and disease assessment. While specialist tools like TotalSegmentator and MRSegmentator achieve strong performance, they require la…
Medical Image SegmentationMaLViL: Multi-axis Low-rank Vision-LSTM for Medical Image Segmentation
Vision-LSTM (ViL) enables efficient global modeling, but its cost still scales with the number of spatial tokens, so existing segmenters confine ViL to a coarse bottleneck and lose fine anatomical detail. Rasterizing 2D …
Medical Image SegmentationTEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake
Deep snake is a promising family of instance segmentation methods that accurately predicts object-level contours, thereby overcoming common pixel-level misclassification issues such as mask cavities and jagged edges in s…
Medical Image SegmentationInstance SegmentationSemantic SegmentationAdaptive Post-Processing Drives Instance-Level Detection in Stroke Lesion Segmentation
Instance-level lesion detection has been an increasingly larger focal point in medical image segmentation besides the more standard voxel-level overlap. Still, most pipelines are trained and post-processed for voxel over…
Medical Image SegmentationLesion SegmentationSUGFW+: An Uncertainty-guided Feature Weighting Framework for Cold Start Active Adaptation of SAM in Medical Image Segmentation
Cold Start Active Learning (CSAL) is important in improving the performance of a medical image segmentation model with low annotation budget by querying a small subset for annotation from an unlabeled training set. Exist…
Medical Image SegmentationSelf-Supervised LearningActive LearningCuria-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation
Radiology foundation models learn transferable representations that can be adapted to new tasks by training only small layers on top of a frozen encoder. Dense prediction tasks such as 3D segmentation are, however, under…
Medical Image SegmentationDistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation
Cross-modal alignment of visual and textual representations is fundamental to multimodal medical image understanding, yet remains hindered by uncertainty in both modalities under real-world clinical conditions. Existing …
Medical Image SegmentationBenchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation
Few-shot medical image segmentation (FS-MIS) aims to segment novel regions of interest (ROIs) from a few annotated support examples. Despite rapid progress, existing FS-MIS solutions span diverse paradigms but are evalua…
Medical Image SegmentationDomain AdaptationAuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans
Fine-grained segmentation of auricular structures in CT is challenging because the ear occupies a small image region, cartilage boundaries are highly irregular, and interfaces between cartilage and surrounding soft tissu…
Medical Image Segmentation