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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.
MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation
MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture
Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information
APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation
Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation
Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models
Automated ischemic stroke lesion segmentation from 3D MRI
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness
AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT
AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CT
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers
A survey, review, and future trends of skin lesion segmentation and classification
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions
ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.