Papers Skin Cancer Segmentation
“Skin Cancer Segmentation” 태그가 달린 논문 14편 · 필터 해제
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+1Training on Polar Image Transformations Improves Biomedical Image Segmentation
A key step in medical image-based diagnosis is image segmentation. A common use case for medical image segmentation is the identification of single structures of an elliptical shape. Most organs like the heart and kidney…
Image SegmentationLesion SegmentationLiver SegmentationMedical Image Segmentation+3DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image …
Cell SegmentationColorectal Polyps CharacterizationImage SegmentationLesion Segmentation+5Skin Lesion Segmentation using SegNet with Binary Cross-Entropy
In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional spe…
Lesion SegmentationSkin Cancer SegmentationSkin Lesion SegmentationSkin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
In the last few years, Deep Learning (DL) has been showing superior performance in different modalities of biomedical image analysis. Several DL architectures have been proposed for classification, segmentation, and dete…
Cancer ClassificationClassificationGeneral ClassificationImage Segmentation+4FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation…
Image SegmentationLesion SegmentationMedical Image SegmentationSegmentation+2Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classific…
image-classificationImage ClassificationImage SegmentationLesion Segmentation+7Road Extraction by Deep Residual U-Net
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and…
Lesion SegmentationLung Nodule SegmentationSemantic SegmentationSkin Cancer SegmentationU-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmenta…
Cell SegmentationCell TrackingColorectal Gland Segmentation:Crack Segmentation+14