Papers with Code 아카이브
Skin Lesion Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation
ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation
Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging
An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation
ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation
GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis
Image Segmentation: Inducing graph-based learning
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation
QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
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
When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation
A Multimodal Vision Foundation Model for Clinical Dermatology
SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance
SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation
AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation
MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation
LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation
TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation
Segmentation Style Discovery: Application to Skin Lesion Images
S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images
MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation
AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation
UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation
Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation
SLP-Net:An efficient lightweight network for segmentation of skin lesions
MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion Segmentation
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification
A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation
IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.