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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.
Skip and Skip: Segmenting Medical Images with Prompts
Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation
MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation
A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts
D-TrAttUnet: Toward Hybrid CNN-Transformer Architecture for Generic and Subtle Segmentation in Medical Images
AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation
An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images
Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment
Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
Synthetic Data for Robust Stroke Segmentation
UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion
Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations
LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models
H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos
Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation
Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation
ProMISe: Promptable Medical Image Segmentation using SAM
Multi-organ Self-supervised Contrastive Learning for Breast Lesion Segmentation
wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7T
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention
Vivim: a Video Vision Mamba for Medical Video Segmentation
Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs
Slicer Networks
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.