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Lesion Segmentation

Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.

( Image credit: D-UNet )

Papers

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Showing 526–550 of 640 papers

TitleStatusHype
Generating Highly Realistic Images of Skin Lesions with GANs—0
Geometric Loss for Deep Multiple Sclerosis lesion Segmentation—0
GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy—0
Global and Local Information Based Deep Network for Skin Lesion Segmentation—0
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images—0
GradMask: Reduce Overfitting by Regularizing Saliency—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
HACA3: A Unified Approach for Multi-site MR Image Harmonization—0
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation—0
HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation—0
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT—0
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework—0
HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation—0
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images—0
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions—0
IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation—0
ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results—0
II-FCN for skin lesion analysis towards melanoma detection—0
Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams—0
Improved Inference via Deep Input Transfer—0
Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data—0
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation—0
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks—0
Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge—0
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
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