Papers Partially Labeled Datasets
“Partially Labeled Datasets” 태그가 달린 논문 29편 · 필터 해제
Multi-Task Self-Training for Learning General Representations
Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision. Here we introduce multi-task self-train…
3D geometryMulti-Task LearningPartially Labeled DatasetsSurface Normal EstimationIncremental Learning for Multi-organ Segmentation with Partially Labeled Datasets
There exists a large number of datasets for organ segmentation, which are partially annotated, and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating th…
Incremental LearningOrgan SegmentationPartially Labeled DatasetsSegmentationMachine Learning Algorithms for Data Labeling: An Empirical Evaluation
The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were d…
Active LearningBIG-bench Machine LearningPartially Labeled DatasetsDoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of organs and/or tumors, resulting in the so-c…
Image SegmentationMedical Image SegmentationOrgan SegmentationPartially Labeled Datasets+3Learning 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 accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For exam…
Lesion DetectionPartially Labeled DatasetsTransfer LearningMulti-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction
Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation. In this paper, we propo…
Image SegmentationOrgan SegmentationPartially Labeled DatasetsSegmentation+1Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from experienced radiologists, it is common t…
Medical Image SegmentationOrgan SegmentationPartially Labeled DatasetsSegmentationA Summary Of The Kernel Matrix, And How To Learn It Effectively Using Semidefinite Programming
Kernel-based learning algorithms are widely used in machine learning for problems that make use of the similarity between object pairs. Such algorithms first embed all data points into an alternative space, where the inn…
Partially Labeled DatasetsWeakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts
We present a weakly-supervised approach to semantic segmentation. The goal is to assign pixel-level labels given only partial information, for example, image-level labels. This is an important problem in many application…
ClusteringPartially Labeled DatasetsSegmentationSemantic Segmentation