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Lung Nodule Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Unified Multi-Scale Attention-Based Network for Automatic 3D Segmentation of Lung Parenchyma & Nodules In Thoracic CT Images
Longitudinal Assessment of Lung Lesion Burden in CT
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning
S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation
Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation
Hierarchical Object-Centric Learning with Capsule Networks
INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data
MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan
Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning
Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Dual-branch residual network for lung nodule segmentation
XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction
Road Extraction by Deep Residual U-Net
Filter sharing: Efficient learning of parameters for volumetric convolutions
U-Net: Convolutional Networks for Biomedical Image Segmentation
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