Skin Cancer Segmentation
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Benchmarks
PH2
Most implemented
U-Net: Convolutional Networks for Biomedical Image Segmentation
Road Extraction by Deep Residual U-Net
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
Papers
Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology
Histopathology image segmentation is essential for delineating tissue structures in skin cancer diagnostics, but modeling spatial context and inter-tissue relationships remains a challenge, especially in regions with ove…
Skin Cancer SegmentationGraph Neural NetworkImage SegmentationMambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation
Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. …
DiagnosticLesion SegmentationMambaSegmentation+2MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation
Skin cancer segmentation poses a significant challenge in medical image analysis. Numerous existing solutions, predominantly CNN-based, face issues related to a lack of global contextual understanding. Alternatively, som…
Image SegmentationLesion SegmentationMedical Image AnalysisMedical Image Segmentation+3Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System
Skin cancer is a global health concern, necessitating early and accurate diagnosis for improved patient outcomes. This study introduces a groundbreaking approach to skin cancer classification, employing the Vision Transf…
Cancer ClassificationDeep LearningSkin Cancer ClassificationSkin Cancer SegmentationAn Interpretable Deep Learning Approach for Skin Cancer Categorization
Skin cancer is a serious worldwide health issue, precise and early detection is essential for better patient outcomes and effective treatment. In this research, we use modern deep learning methods and explainable artific…
Decision MakingDeep LearningDiagnosticExplainable artificial intelligence+4SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model
Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment. Although manual tracing is the current standard in clinics, automated tools are desired to reduce hum…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1