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Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach
Using radiological scans to identify liver tumors is crucial for proper ...
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Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
Monitoring treatment response in longitudinal studies plays an important...
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Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
Radiological images such as computed tomography (CT) and X-rays render a...
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Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
Large-scale datasets with high-quality labels are desired for training a...
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User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
Mask-based annotation of medical images, especially for 3D data, is a bo...
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Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
Identifying, measuring and reporting lesions accurately and comprehensiv...
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Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Determining the spread of GTV_LN is essential in defining the respective...
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Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Although having achieved great success in medical image segmentation, de...
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Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
Effective and non-invasive radiological imaging based tumor/lesion chara...
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Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
Lesion detection is an important problem within medical imaging analysis...
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Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
Finding and identifying scatteredly-distributed, small, and critically i...
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Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation
In medical imaging, organ/pathology segmentation models trained on curre...
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Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
Acquiring large-scale medical image data, necessary for training machine...
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End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many ...
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3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
We propose a novel framework, uncertainty-aware multi-view co-training (...
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CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Automated lesion segmentation from computed tomography (CT) is an import...
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Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays
Given image labels as the only supervisory signal, we focus on harvestin...
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Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Volumetric lesion segmentation from computed tomography (CT) images is a...
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Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Automatic pancreas segmentation in radiology images, eg., computed tomog...
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Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Volumetric lesion segmentation via medical imaging is a powerful means t...
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Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
Deep neural networks have demonstrated very promising performance on acc...
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