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Volumetric Medical Image Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Zero-Shot Gaze-based Volumetric Medical Image Segmentation
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2
Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer
HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing
Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
SegVol: Universal and Interactive Volumetric Medical Image Segmentation
CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation
SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder
Learnable Weight Initialization for Volumetric Medical Image Segmentation
Volumetric medical image segmentation through dual self-distillation in U-shaped networks
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift
Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma
Distributed Contrastive Learning for Medical Image Segmentation
Implicit U-Net for volumetric medical image segmentation
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation
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