- Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation
- Brain Tumor Segmentation with Deep Neural Networks
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
- CNN-based Segmentation of Medical Imaging Data
- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
- On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
- 3D Densely Convolutional Networks for Volumetric Segmentation
- Spinal cord gray matter segmentation using deep dilated convolutions
- Cost-Effective Active Learning for Melanoma Segmentation
- Detection-aided liver lesion segmentation using deep learning
- Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
- Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
- Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
- Replication study: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
- An application of cascaded 3D fully convolutional networks for medical image segmentation
- Factorised spatial representation learning: application in semi-supervised myocardial segmentation
- HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
- Attention U-Net: Learning Where to Look for the Pancreas
- Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning
- Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
- Constrained-CNN Losses for Weakly Supervised Segmentation
- Autofocus Layer for Semantic Segmentation
- Y-Net: Joint Segmentation and Classi cation for Diagnosis of Breast Biopsy Images
- A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation
- Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
- UNet++: A Nested U-Net Architecture for Medical Image Segmentation
- nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
- LadderNet: Multi-path networks based on U-Net for medical image segmentation
- A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation
- Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
- RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
- Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
- IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet
- Elastic Boundary Projection for 3D Medical Image Segmentation
- Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model
- Boundary loss for highly unbalanced segmentation
- PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
- Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
- Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
- FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
- Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
- MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
- Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
- Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
- Data augmentation using learned transformations for one-shot medical image segmentation
- CE-Net: Context Encoder Network for 2D Medical Image Segmentation
- From Patch to Image Segmentation using Fully Convolutional Networks - Application to Retinal Images
- Automated Design of Deep Learning Methods for Biomedical Image Segmentation
- Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks
- Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
- One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
- Multi-scale self-guided attention for medical image segmentation
- When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
- PHiSeg: Capturing Uncertainty in Medical Image Segmentation
- A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants
- Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
- CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
- Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation
- U-Net Fixed-Point Quantization for Medical Image Segmentation
- D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation
- Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
- Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
- 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
- Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
- MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
- Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
- Domain Generalization via Model-Agnostic Learning of Semantic Features
- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice
- One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
- Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
- ResUNet++: An Advanced Architecture for Medical Image Segmentation
- Segmenting Medical MRI via Recurrent Decoding Cell
- UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
- Transfer Learning for Brain Tumor Segmentation
- A context based deep learning approach for unbalanced medical image segmentation
- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
- Medical Image Segmentation via Unsupervised Convolutional Neural Network
- Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
- Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
- Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation
- ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
- Test-time adaptable neural networks for robust medical image segmentation
- A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
- UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
- Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
- ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation
- A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
- Segmentation Loss Odyssey
- Uncertainty quantification in medical image segmentation with normalizing flows
- DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
- KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
- Contrastive learning of global and local features for medical image segmentation with limited annotations
- PraNet: Parallel Reverse Attention Network for Polyp Segmentation
- Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
- An Elastic Interaction-Based Loss Function for Medical Image Segmentation
- Scribble-based Domain Adaptation via Co-segmentation
- MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts
- Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
- On uncertainty estimation in active learning for image segmentation
- Self-supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
- Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
- Learning Directional Feature Maps for Cardiac MRI Segmentation
- MACU-Net: Semantic Segmentation from High-Resolution Remote Sensing Images
- Learning To Pay Attention To Mistakes
- Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
- A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
- Semi-supervised Medical Image Segmentation through Dual-task Consistency
- Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans
- CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
- KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
- Selective Information Passing for MR/CT Image Segmentation
- GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
- Learning Euler's Elastica Model for Medical Image Segmentation
- Contrastive Registration for Unsupervised Medical Image Segmentation
- Inter-slice Context Residual Learning for 3D Medical Image Segmentation
- HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images
- Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
buiduylinh87 / medical-image-segmentation-papers Goto Github PK
View Code? Open in Web Editor NEWThis project forked from manjunath5496/medical-image-segmentation-papers
"Undeniable chemistry and horrific timing. They love each other."― Darnell Lamont Walker