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Lesion Segmentation
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Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
<span style="color:grey; opacity: 0.6">( Image credit: D-UNet )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention
Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging
Automated external cervical resorption segmentation in cone-beam CT using local texture features
Image Segmentation: Inducing graph-based learning
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities
{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation
Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation
TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation
Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence
Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology
Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach
When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation
Automated Segmentation of Ischemic Stroke Lesions in Non-Contrast Computed Tomography Images for Enhanced Treatment and Prognosis
HRDecoder: High-Resolution Decoder Network for Fundus Image Lesion Segmentation
Disentangled PET Lesion Segmentation
SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
Toward Generalizable Multiple Sclerosis Lesion Segmentation Models
Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment
Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
A Multimodal Vision Foundation Model for Clinical Dermatology
ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results
Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
3DPX: Single Panoramic X-ray Analysis Guided by 3D Oral Structure Reconstruction
XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.