zeroth/Medical-Image-Segmentation-Papers

"Undeniable chemistry and horrific timing. They love each other."― Darnell Lamont Walker

Medical Image Segmentation Papers

• 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