讲在前面
这部分是PART I和PART II。
论文目录
PART I
| Image Quality and Artefacts（图像质量和伪影） | 概要 |
|---|---|
| 1.Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear 用于减少耳朵CT图像中的金属伪像的条件生成对抗网络 |
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| 2.Neural Network Evolution Using Expedited Genetic Algorithm for Medical Image Denoising 基于加速遗传算法的神经网络进化医学图像降噪 |
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| 3.Deep Convolutional Filtering for Spatio-Temporal Denoising and Artifact Removal in Arterial Spin Labelling MRI 深卷积滤波在动脉自旋标记MRI中的时空降噪和伪像去除 |
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| 4.DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning DeepASL：通过深度残差学习对动脉自旋标记MRI去噪的动力学模型合并损失 |
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| 5.Direct Estimation of Pharmacokinetic Parameters from DCE-MRI Using Deep CNN with Forward Physical Model Loss 使用深度CNN和正向物理模型损失从DCE-MRI直接估算药代动力学参数 |
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| 6.Short Acquisition Time PET/MR Pharmacokinetic Modelling Using CNNs 使用CNN的短采集时间PET / MR药代动力学建模 |
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| 7.Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements? 深度学习能否放松内窥镜检查对硬件小型化的要求？ |
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| 8.A Framework to Objectively Identify Reference Regions for Normalizing Quantitative Imaging 用欠采样传感器评估光声层析成像中的伴随方法 |
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| 9.Evaluation of Adjoint Methods in Photoacoustic Tomography with Under-Sampled Sensors 用欠采样传感器评估光声层析成像中的伴随方法 |
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| 10.A No-Reference Quality Metric for Retinal Vessel Tree Segmentation 视网膜血管树分割的无参考质量指标 |
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| 11.Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-level Densely Connected Network 使用生成对抗网络和3D多级密集连接网络进行高效，准确的MRI超分辨率 |
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| 12.A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRI 基于深度学习的MRI抗混叠自我超分辨率算法 |
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| 13.Gradient Profile Based Super Resolution of MR Images with Induced Sparsity 基于梯度轮廓的具有稀疏性的MR图像超分辨率 |
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| 14.Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images 更深入的图像质量传递：训练用于3D图像的低内存神经网络 |
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| 15.High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning 具有深度学习功能的高帧频心脏超声成像 |
| Image Reconstruction Methods（图像重建方法） | 概要 |
|---|---|
| 16.Phase-Sensitive Region-of-Interest Computed Tomography 相位敏感的兴趣区域计算断层扫描 |
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| 17.Some Investigations on Robustness of Deep Learning in Limited Angle Tomography 有限角质断层扫描深度学习鲁棒性的研究 |
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| 18.Adversarial Sparse-View CBCT Artifact Reduction 对抗稀疏视图CBCT伪影减少 |
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| 19.Nasal Mesh Unfolding – An Approach to Obtaining 2-D Skin Templates from 3-D Nose Models 鼻网展开 - 从3-D鼻模型获得2-D皮肤模板的方法 |
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| 20.Towards Generating Personalized Volumetric Phantom from Patient’s Surface Geometry 从患者的表面几何形状产生个性化体积幻像 |
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| 21.Multi-channel Generative Adversarial Network for Parallel Magnetic Resonance Image Reconstruction in K-space 用于k空间的并联磁共振图像重建多通道生成对抗网络 |
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| 22.A Learning-Based Metal Artifacts Correction Method for MRI Using Dual-Polarity Readout Gradients and Simulated Data 使用双极读出梯度和模拟数据的MRI的基于学习的金属伪影校正方法 |
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| 23.Motion Aware MR Imaging via Spatial Core Correspondence Motion Inveln MR Imaging通过空间核心通信 |
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| 24.Nonparametric Density Flows for MRI Intensity Normalisation 用于MRI强度标准化的非参数密度流动 |
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| 25.Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information 超快速T2加权MR重建使用互补T1加权信息 |
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| 26.Image Reconstruction by Splitting Deep Learning Regularization from Iterative Inversion 从迭代反演分离深度学习正规的图像重建 |
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| 27.Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction 压缩传感MRI重建的对抗和感知细化 |
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| 28.Translation of 1D Inverse Fourier Transform of K-space to an Image Based on Deep Learning for Accelerating Magnetic Resonance Imaging 基于深度学习加速磁共振成像的1D逆傅里叶变换的k空间逆傅里叶变换的翻译 |
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| 29.Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection 基于K空间的数据增强的自动化心MR运动人工制品检测深度学习 |
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| 30.Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning 使用深层潜在的代表学习，来自欠采样的K空间的心脏MR分段 |
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| 31.A Comprehensive Approach for Learning-Based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks 一种综合方法，用于基于学习的基于自动化切片切片片运动校正，用于短轴电影心脏MR图像堆栈 |
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| 32.Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents 用多尺寸深加固学习代理自动浏览规划 |
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| 33.Towards MR-Only Radiotherapy Treatment Planning: Synthetic CT Generation Using Multi-view Deep Convolutional Neural Networks 朝着仅仅是无放射治疗计划：使用多视图深卷积神经网络的合成CT生成 |
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| 34.Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI 随机深度抗压感，用于重建扩散张力心脏MRI |
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| 35.Automatic, Fast and Robust Characterization of Noise Distributions for Diffusion MRI 用于扩散MRI的噪声分布的自动，快速且坚固的表征 |
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| 36.An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI 胎儿脑MRI自动定位，分割和重建框架 |
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| 37.Retinal Image Understanding Emerges from Self-Supervised Multimodal Reconstruction 视网膜图像理解从自我监督的多式化重建中出现 |
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| 38.Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis 用于高质量PET图像合成的地区自适应多种式GAN |
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| 39.Joint PET+MRI Patch-Based Dictionary for Bayesian Random Field PET Reconstruction 贝叶斯随机场宠物重建的联合宠物+ MRI补丁词典 |
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| 40.Analysis of 3D Facial Dysmorphology in Genetic Syndromes from Unconstrained 2D Photographs 无约束2D拍摄遗传综合征的3D面部缺血性分析 |
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| 41.Double Your Views – Exploiting Symmetry in Transmission Imaging 双倍视图 - 在传输成像中利用对称性 |
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| 42.Real Time RNN Based 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip 实时基于RNN的3D超声扫描充足性，用于髋部发育发育不良 |
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| 43.Direct Reconstruction of Ultrasound Elastography Using an End-to-End Deep Neural Network 使用端到端深神经网络直接重建超声弹性术 |
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| 44.3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks 3D通过深度条件生成网络从2Dus重建3D胎头颅骨重建 |
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| 45.Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network 使用迭代变换网络的3D胎儿超声标准平面检测 |
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| 46.Towards Radiotherapy Enhancement and Real Time Tumor Radiation Dosimetry Through 3D Imaging of Gold Nanoparticles Using XFCT 通过使用XFCT的金纳米粒子的3D成像来朝向放射疗法增强和实时肿瘤辐射剂量测定 |
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| 47.Dual-Domain Cascaded Regression for Synthesizing 7T from 3T MRI 从3T MRI合成7T的双域级联回归 |
| Machine Learning in Medical Imaging（医学影像中的机器学习） | 概要 |
|---|---|
| 48.Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks 完全卷积网络中的并发空间和渠道“挤压和激发” |
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| 49.SPNet: Shape Prediction Using a Fully Convolutional Neural Network Spnet：使用完全卷积神经网络的形状预测 |
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| 50.Roto-Translation Covariant Convolutional Networks for Medical Image Analysis 旋转转换协会的医学图像分析卷积网络 |
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| 51.Bimodal Network Architectures for Automatic Generation of Image Annotation from Text Bimodal网络架构，用于从文本中自动生成图像注释 |
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| 52.Multimodal Recurrent Model with Attention for Automated Radiology Report Generation 自动放射学前的多式联运模型 |
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| 53.Magnetic Resonance Spectroscopy Quantification Using Deep Learning 磁共振光谱法测量使用深度学习 |
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| 54.A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols 跨越扫描仪和协议的脑MR细分终身学习方法 |
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| 55.Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations 响应凸轮：通过可视化分析3D成像数据的深层模型 |
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| 56.Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples 普遍性与鲁棒性：使用对抗示例调查医学成像网络 |
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| 57.Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector 主题2VEC：从一组图像修补程序到向量的生成 - 鉴别方法 |
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| 58.3D Context Enhanced Region-Based Convolutional Neural Network for End-to-End Lesion Detection 3D上下文增强的基于区域的卷积神经网络，用于端到端病变检测 |
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| 59.Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks 保留和学习：通过限制神经网络中知识保存的潜空间，继续学习 |
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| 60.Distribution Matching Losses Can Hallucinate Features in Medical Image Translation 分布匹配损失可以幻觉在医学图像翻译中的功能 |
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| 61.Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation 生成可逆网络（GIN）：病理生理学可解释的特征映射和虚拟患者生成 |
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| 62.Training Medical Image Analysis Systems like Radiologists 培训辐射学家等医学图像分析系统 |
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| 63.Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer’s Disease 联合高阶多任务特征学习预测阿尔茨海默病的进展 |
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| 64.Fast Multiple Landmark Localisation Using a Patch-Based Iterative Network 使用贴片式迭代网络快速多地标定位 |
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| 65.Omni-Supervised Learning: Scaling Up to Large Unlabelled Medical Datasets Omni监督学习：扩展到大型未标记的医疗数据集 |
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| 66.Recurrent Neural Networks for Classifying Human Embryonic Stem Cell-Derived Cardiomyocytes 用于分类人胚胎干细胞衍生心肌细胞的经常性神经网络 |
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| 67.Group-Driven Reinforcement Learning for Personalized mHealth Intervention 个性化MHE健康干预的集团驱动的钢筋学习 |
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| 68.Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes 浅脑多路复用痴呆症分层联合关联和鉴别合奏分类器学习 |
| Statistical Analysis for Medical Imaging（医学影像统计分析） | 概要 |
|---|---|
| 69.FDR-HS: An Empirical Bayesian Identification of Heterogenous Features in Neuroimage Analysis FDR-HS：神经造影分析中异质特征的经验贝叶斯鉴定 |
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| 70.Order-Sensitive Deep Hashing for Multimorbidity Medical Image Retrieval 多元化医学图像检索的订单敏感深层散 |
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| 71.Exact Combinatorial Inference for Brain Images 精确组合脑图像的组合推论 |
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| 72.Statistical Inference with Ensemble of Clustered Desparsified Lasso 统计推断与集群Deparsified Lasso的集群 |
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| 73.Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification 多中心自闭症谱系识别的低秩表示 |
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| 74.Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation 探索深网络深度网络的不确定性措施，进行多发性硬化病变检测和分割 |
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| 75.Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling 完全Convnet Monte Carlo采样的固有脑细分质量控制 |
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| 76.Perfect MCMC Sampling in Bayesian MRFs for Uncertainty Estimation in Segmentation 贝叶斯MRF中的完美MCMC采样，用于分割的不确定性估算 |
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| 77.On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation 观察者间变异性对医学图像分割不确定度的可靠估计的影响 |
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| 78.Towards Safe Deep Learning: Accurately Quantifying Biomarker Uncertainty in Neural Network Predictions 为了安全深入学习：准确地量化神经网络预测中的生物标志物不确定性 |
| Image Registration Methods（图像配准方法） | 概要 |
|---|---|
| 79.Registration-Based Patient-Specific Musculoskeletal Modeling Using High Fidelity Cadaveric Template Model 基于注册的患者特异性肌肉骨骼建模使用高保真尸体模板模型 |
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| 80.Atlas Propagation Through Template Selection 通过模板选择传播图表传播 |
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| 81.Spatio-Temporal Atlas of Bone Mineral Density Ageing 骨矿物密度老化的时空地图集 |
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| 82.Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration 无监督的快速学习快速概率弥漫性注册 |
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| 83.Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration 基于深度学习注册中的图像对齐评估图像对齐的对抗性相似网络 |
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| 84.Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries 通过高估改善手术训练幽灵：来自真正手术的深度未配对的图像到图像翻译 |
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| 85.Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry 用黎曼几何计算CNN损耗和梯度姿态估算 |
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| 86.GDL-FIRE 4D : Deep Learning-Based Fast 4D CT Image Registration GDL-Fire 4D：基于深度学习的快速4D CT图像配准 |
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| 87.Adversarial Deformation Regularization for Training Image Registration Neural Networks 对训练图像登记神经网络的对抗变形正规化 |
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| 88.Fast Registration by Boundary Sampling and Linear Programming 边界采样快速注册和线性规划 |
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| 89.Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis 从RGB-D数据学习一个婴儿车身模型，以准确全身运动分析 |
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| 90.Consistent Correspondence of Cone-Beam CT Images Using Volume Functional Maps 使用体积函数映射的锥形光束CT图像的一致对应关系 |
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| 91.Elastic Registration of Geodesic Vascular Graphs 弹性血管图的弹性登记 |
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| 92.Efficient Groupwise Registration of MR Brain Images via Hierarchical Graph Set Shrinkage 通过分层图集合缩收有效地扩展MR脑图像的脑图像 |
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| 93.Initialize Globally Before Acting Locally: Enabling Landmark-Free 3D US to MRI Registration 在当地行用之前在全球范围内进行初始化：使载体免费3D向MRI注册启用 |
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| 94.Solving the Cross-Subject Parcel Matching Problem Using Optimal Transport 使用最佳运输来解决交叉主题包裹匹配问题 |
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| 95.GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport Glymphvis：使用正则化最佳运输可视化Glymphatic传输途径 |
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| 96.Hierarchical Spherical Deformation for Shape Correspondence 形状对应的分层球形变形 |
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| 97.Diffeomorphic Brain Shape Modelling Using Gauss-Newton Optimisation 使用Gauss-Newton优化的扩散脑形状模拟 |
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| 98.Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps 多项任务SOOOEYENET：检测由生成的超声波辅助地图辅助的胎儿标准化飞机 |
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| 99.Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification 贝叶斯注册不确定性量化的高效LAPLACE近似 |
PART II
| Optical and Histology Applications: Optical Imaging Applications)（ 光学和组织学应用：光学成像应用） | 概要 |
|---|---|
| 1.Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks 使用余弦嵌入和经常性沙漏网络进行实例分割和跟踪 |
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| 2.Skin Lesion Classification in Dermoscopy Images Using Synergic Deep Learning 利用协同深度学习皮肤病患者皮肤病变分类 |
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| 3.SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks SLSDeep：基于扩张残差和金字塔池网络的皮肤病变分割 |
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| 4.β -Hemolysis Detection on Cultured Blood Agar Plates by Convolutional Neural Networks β-溶血性神经网络培养血液琼脂平板β-泡沫检测 |
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| 5.A Pixel-Wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection 具有关节视网膜光盘和FOVEA检测的像素方面距离回归方法 |
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| 6.Deep Random Walk for Drusen Segmentation from Fundus Images 来自眼底图像的Drusen分割深呼随机散步 |
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| 7.Retinal Artery and Vein Classification via Dominant Sets Clustering-Based Vascular Topology Estimation 通过优势集基于聚类的血管拓扑估算视网膜动脉和静脉分类 |
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| 8.Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images 基于从眼底图像的模拟血流动力学朝向青光眼风险指数 |
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| 9.A Framework for Identifying Diabetic Retinopathy Based on Anti-noise Detection and Attention-Based Fusion 基于抗噪声检测和基于关注的融合来识别糖尿病视网膜病变的框架 |
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| 10.Deep Supervision with Additional Labels for Retinal Vessel Segmentation Task 具有视网膜船分割任务的额外标签的深度监督 |
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| 11.A Multi-task Network to Detect Junctions in Retinal Vasculature 多任务网络检测视网膜脉管系统中的结 |
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| 12.A Multitask Learning Architecture for Simultaneous Segmentation of Bright and Red Lesions in Fundus Images 一种多任务学习架构，用于在眼底图像中同时分割明亮和红色病变的分割 |
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| 13.Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases 自动化病变检测的唯一性驱动显着性分析与视网膜疾病的应用 |
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| 14.Multiscale Network Followed Network Model for Retinal Vessel Segmentation 多尺度网络跟随视网膜船分段的网络模型 |
| Optical and Histology Applications: Histology Applications（光学和组织学应用：组织学应用） | 概要 |
|---|---|
| 15.Predicting Cancer with a Recurrent Visual Attention Model for Histopathology Images 预测组织病理学图像的反复视觉注意模型 |
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| 16.A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation 对压盖实例分割的形状保存损失的深层模型 |
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| 17.Model-Based Refinement of Nonlinear Registrations in 3D Histology Reconstruction 3D组织学重建中非线性注册的基于模型改进 |
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| 18.Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning 侵入性癌症检测利用压缩卷积神经网络和转移学习 |
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| 19.Which Way Round? A Study on the Performance of Stain-Translation for Segmenting Arbitrarily Dyed Histological Images 哪个方式？分段任意染色组织学图像染色翻译性能研究 |
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| 20.Graph CNN for Survival Analysis on Whole Slide Pathological Images 图CNN用于整体幻灯片病理图像的存活分析 |
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| 21.Fully Automated Blind Color Deconvolution of Histopathological Images 组织病理学图像的全自动盲颜色去卷积 |
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| 22.Improving Whole Slide Segmentation Through Visual Context - A Systematic Study 通过视觉背景改善整个幻灯片分割 - 系统研究 |
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| 23.Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images 前列腺组织病理学分类的对抗域适应全幻灯片 |
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| 24.Rotation Equivariant CNNs for Digital Pathology 用于数字病理学的旋转等级CNN |
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| 25.A Probabilistic Model Combining Deep Learning and Multi-atlas Segmentation for Semi-automated Labelling of Histology 组织学半自动标记的深度学习与多拟标题分割的概率模型 |
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| 26.BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images BESNET：组织病理学图像中细胞的边界增强分割 |
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| 27.Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis 用端到端小区R-CNN进行PANoptic分割，用于病理图像分析 |
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| 28.Integration of Spatial Distribution in Imaging-Genetics 成像遗传学中空间分布的整合 |
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| 29.Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology 异构图像的多实例学习：培训组织病理学的CNN |
| Optical and Histology Applications: Microscopy Applications（光学和组织学应用：显微镜应用） | 概要 |
|---|---|
| 30.Cell Detection with Star-Convex Polygons 用星形凸多边形检测电池检测 |
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| 31.Deep Convolutional Gaussian Mixture Model for Stain-Color Normalization of Histopathological Images 组织病理学图像的染色标准化深卷积高斯混合模型 |
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| 32.Learning to Segment 3D Linear Structures Using Only 2D Annotations 学习仅使用2D注释进行分段3D线性结构 |
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| 33.A Multiresolution Convolutional Neural Network with Partial Label Training for Annotating Reflectance Confocal Microscopy Images of Skin 具有部分标记训练的多分辨率卷积神经网络，用于注释皮肤的反射率分组显微镜图像 |
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| 34.Weakly-Supervised Learning-Based Feature Localization for Confocal Laser Endomicroscopy Glioma Images 基于弱监督的基于学习的学习功能定位，用于共聚焦激光子宫内膜透视胶质瘤图像 |
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| 35.Synaptic Partner Prediction from Point Annotations in Insect Brains 昆虫大脑中点注释的突触伴侣预测 |
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| 36.Synaptic Cleft Segmentation in Non-isotropic Volume Electron Microscopy of the Complete Drosophila Brain 完全果蝇脑的非各向同性体积电子显微镜中的突触裂缝分割 |
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| 37.Weakly Supervised Representation Learning for Endomicroscopy Image Analysis 用于内窥镜图像分析的弱监督代表学习 |
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| 38.DeepHCS: Bright-Field to Fluorescence Microscopy Image Conversion Using Deep Learning for Label-Free High-Content Screening DeepHCS：使用深度学习的无标记高含量筛选的荧光显微镜图像转换的明亮场 |
| Optical and Histology Applications: Optical Coherence Tomography and Other Optical Imaging Applications（光学和组织学应用：光学相干断层扫描和其他光学成像应用） | 概要 |
|---|---|
| 39.A Cascaded Refinement GAN for Phase Contrast Microscopy Image Super Resolution 用于相位对比显微镜图像超分辨率的级联细化GaN |
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| 40.Multi-context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT OCT中的角度闭合青光眼筛选的多语境深网络 |
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| 41.Analysis of Morphological Changes of Lamina Cribrosa Under Acute Intraocular Pressure Change 急性眼压变化下椎板克里泽的形态变化分析 |
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| 42.Beyond Retinal Layers: A Large Blob Detection for Subretinal Fluid Segmentation in SD-OCT Images 超越视网膜层：SD-OCT图像中的子靶流体分割的大BLOB检测 |
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| 43.Automated Choroidal Neovascularization Detection for Time Series SD-OCT Images 时间序列SD-OCT图像的自动脉络膜新生血管检测 |
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| 44.CapsDeMM: Capsule Network for Detection of Munro’s Microabscess in Skin Biopsy Images Capsdemm：胶囊网络检测皮肤活检图像中的Munro微臂 |
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| 45.Webly Supervised Learning for Skin Lesion Classification 对皮肤病变分类进行扫视学习 |
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| 46.Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer 功能驱动的本地单元格图（FEDEG）：预测早期肺癌的整体生存 |
| Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications（心脏，胸部和腹部应用：心脏成像应用） | 概要 |
|---|---|
| 47.Towards Accurate and Complete Registration of Coronary Arteries in CTA Images 在CTA图像中准确和完全注册冠状动脉 |
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| 48.Quantifying Tensor Field Similarity with Global Distributions and Optimal Transport 量化与全局分布和最优运输的张力场相似度 |
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| 49.Cardiac Motion Scoring with Segment- and Subject-Level Non-local Modeling 心动运动与分段和主题非本地建模进行评分 |
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| 50.Computational Heart Modeling for Evaluating Efficacy of MRI Techniques in Predicting Appropriate ICD Therapy 用于评估MRI技术疗效在预测适当ICD治疗中的疗效计算心脏建模 |
|
| 51.Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation 左心房和心房疤痕分割的多视图两项任务递归注意力模型 |
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| 52.Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling 通过深生成模型学习可解释的解剖功能：应用于心脏重塑 |
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| 53.Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences 心肌图像序列运动估计和分割的联合学习 |
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| 54.Multi-Input and Dataset-Invariant Adversarial Learning (MDAL) for Left and Right-Ventricular Coverage Estimation in Cardiac MRI 心脏MRI中左右心室覆盖估计的多输入和数据集不变的对抗性学习（MDAL） |
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| 55.Factorised Spatial Representation Learning: Application in Semi-supervised Myocardial Segmentation 要分的空间代表学习：在半监督心肌细分中的应用 |
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| 56.High-Dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model 通过嵌入式生成模型，高维贝叶斯优化个性化心模范参数 |
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| 57.Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential 心脏跨膜电位的生成建模与逆成像 |
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| 58.Pulmonary Vessel Tree Matching for Quantifying Changes in Vascular Morphology 肺血管树匹配量化血管形态的变化 |
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| 59.MuTGAN: Simultaneous Segmentation and Quantification of Myocardial Infarction Without Contrast Agents via Joint Adversarial Learning mutgan：通过联合对抗学习的同时分割和定量心肌梗死的心肌梗死 |
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| 60.More Knowledge Is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring 更多知识更好：心内超声心动图轮廓的跨模型体积完成和3D + 2D分段 |
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| 61.Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio 无监督的域适应用于自动估计心肌差距 |
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| 62.TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-Rays TEXTRAY：挖掘临床报告，以获得对胸部X光的广泛理解 |
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| 63.Localization and Labeling of Posterior Ribs in Chest Radiographs Using a CRF-regularized FCN with Local Refinement 利用当地改进的CRF - 正则FCN定位和胸部射线照相后肋的定位和标记 |
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| 64.Evaluation of Collimation Prediction Based on Depth Images and Automated Landmark Detection for Routine Clinical Chest X-Ray Exams 基于深度图像的准直预测和常规临床胸部X射线检查的准直预测评估 |
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| 65.Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network 使用样品选择和条件生成对抗网络的图像分类和分割的高效学习 |
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| 66.Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays 弱势监督胸部胸膜疾病定位的迭代注意力挖掘 |
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| 67.Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation 无监督域适应的任务驱动生成模型：应用于X射线图像分割 |
| Cardiac, Chest and Abdominal Applications: Colorectal, Kidney and Liver Imaging Applications（心脏，胸部和腹部的应用：大肠，肾脏和肝脏的成像应用） | 概要 |
|---|---|
| 68.Towards Automated Colonoscopy Diagnosis: Binary Polyp Size Estimation via Unsupervised Depth Learning 朝向自动结肠镜检查诊断：通过无监督深度学习的二元息肉尺寸估计 |
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| 69.RIIS-DenseNet: Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection RIIS-DENSENET：旋转不变和图像相似度约束息肉检测的密集连接卷积网络 |
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| 70.Interaction Techniques for Immersive CT Colonography: A Professional Assessment 沉浸式CT结肠扫描的相互作用技术：专业评估 |
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| 71.Quasi-automatic Colon Segmentation on T2-MRI Images with Low User Effort 具有低用户努力的T2-MRI图像的准自动结肠分割 |
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| 72.Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer 早期肾癌存活分析的序数多模态特征选择 |
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| 73.Noninvasive Determination of Gene Mutations in Clear Cell Renal Cell Carcinoma Using Multiple Instance Decisions Aggregated CNN 多实例决策综合CNN的透明细胞肾细胞癌基因突变的非侵入性测定 |
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| 74.Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-phase CT Images 结合卷积和经常性神经网络在多相CT图像中局灶性肝病变分类 |
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| 75.Construction of a Spatiotemporal Statistical Shape Model of Pediatric Liver from Cross-Sectional Data 跨截面数据施工小儿肝的时空统计形状模型 |
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| 76.Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy 肝脏立体定向体辐射治疗后结果预测的深度3D剂量分析 |
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| 77.Liver Lesion Detection from Weakly-Labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector 利用分组的单次Multibox检测器从弱标记的多相CT卷中检测肝脏病变检测 |
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| 78.A Diagnostic Report Generator from CT Volumes on Liver Tumor with Semi-supervised Attention Mechanism 来自CT体积的肝脏肿瘤对肝脏肿瘤的诊断报告发生器，具有半监控注意机制 |
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| 79.Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images 更少的是：腹部超声图像的同时查看分类和地标检测 |
| Cardiac, Chest and Abdominal Applications: Lung Imaging Applications（心脏，胸部和腹部的应用：肺部成像应用） | 概要 |
|---|---|
| 80.Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation 深度主动自花奏学习，用于准确肺结核分割 |
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| 81.CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation 基于3D条件生成对抗网络的CT - 现实肺结节模拟鲁棒肺分割 |
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| 82.Fast CapsNet for Lung Cancer Screening 肺癌筛选的快速帽 |
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| 83.Mean Field Network Based Graph Refinement with Application to Airway Tree Extraction 基于平凡的网络基于网络改进与呼吸道树提取的应用 |
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| 84.Automated Pulmonary Nodule Detection: High Sensitivity with Few Candidates 自动肺结核检测：具有少数候选者的高灵敏度 |
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| 85.Deep Learning from Label Proportions for Emphysema Quantification 深度学习从标签比例进行肺气肿量化 |
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| 86.Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation 肿瘤感知，来自CT对肺癌分割MRI的对抗域适应 |
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| 87.From Local to Global: A Holistic Lung Graph Model 从当地到全球：整体肺图模型 |
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| 88.S4ND: Single-Shot Single-Scale Lung Nodule Detection S4ND：单次单尺度肺结节检测 |
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| 89.Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response 血管网络组织通过Hough变换（VangoGH）：一种新型射出的辐射生物标志物，用于诊断和治疗反应 |
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| 90.DeepEM: Deep 3D ConvNets with EM for Weakly Supervised Pulmonary Nodule Detection Deepem：深度3D呼应，用于弱监督肺结核检测 |
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| 91.Statistical Framework for the Definition of Emphysema in CT Scans: Beyond Density Mask CT扫描中肺气肿定义的统计框架：超越密度面膜 |
| Cardiac, Chest and Abdominal Applications: Breast Imaging Applications（Cardiac, Chest and Abdominal Applications: Breast Imaging Applications） | 概要 |
|---|---|
| 92.Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification 用于X射线胸部质量分割和形状分类的条件生成对抗和卷积网络 |
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| 93.A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer 患有乳腺癌准确转移检测和PN阶段分类的鲁棒和有效的方法 |
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| 94.3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes 3D各向异性混合网络：将卷积功能从2D图像转移到3D各向异性卷 |
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| 95.Deep Generative Breast Cancer Screening and Diagnosis 深生成的乳腺癌筛查和诊断 |
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| 96.Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis 整合域名知识在培训CNN中进行超声检查乳腺癌癌症诊断 |
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| 97.Small Lesion Classification in Dynamic Contrast Enhancement MRI for Breast Cancer Early Detection 动态对比增强MRI的小病变分类早期检测 |
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| 98.Thermographic Computational Analyses of a 3D Model of a Scanned Breast 扫描乳房3D模型的热敏计算分析 |
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| 99.Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images Y-net：乳腺活检图像诊断的联合分割和分类 |
| Cardiac, Chest and Abdominal Applications: Other Abdominal Applications（心脏，胸部和腹部应用：其他腹部应用） | 概要 |
|---|---|
| 100.AutoDVT: Joint Real-Time Classification for Vein Compressibility Analysis in Deep Vein Thrombosis Ultrasound Diagnostics AutoDVT：深静脉血栓形成超声诊断中静脉压缩性分析的联合实时分类 |
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| 101.MRI Measurement of Placental Perfusion and Fetal Blood Oxygen Saturation in Normal Pregnancy and Placental Insufficiency MRI测量胎盘灌注和胎儿血氧饱和度正常妊娠和胎盘功能不全 |
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| 102.Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation 通过层聚合在胎盘超声图像中自动化LELUNAE定位 |
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| 103.A Decomposable Model for the Detection of Prostate Cancer in Multi-parametric MRI 多参数MRI检测前列腺癌的可分解模型 |
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| 104.Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network 通过级联放大器回归网络直接自动定量测量脊柱 |
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| 105.Estimating Achilles Tendon Healing Progress with Convolutional Neural Networks 用卷积神经网络估算Achilles肌腱愈合进展 |