Papers Partially Labeled Datasets
“Partially Labeled Datasets” 태그가 달린 논문 29편 · 필터 해제
A Continual Learning-driven Model for Accurate and Generalizable Segmentation of Clinically Comprehensive and Fine-grained Whole-body Anatomies in CT
Precision medicine in the quantitative management of chronic diseases and oncology would be greatly improved if the Computed Tomography (CT) scan of any patient could be segmented, parsed and analyzed in a precise and de…
Computed Tomography (CT)Continual LearningPartially Labeled DatasetsPrivacy Preservation through Practical Machine Unlearning
Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative…
Machine UnlearningPartially Labeled DatasetsFederated Learning with Partially Labeled Data: A Conditional Distillation Approach
In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict privacy regulations present significant …
Federated LearningImage SegmentationMedical Image SegmentationPartially Labeled Datasets+2Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
This research addresses the need for high-definition (HD) maps for autonomous vehicles (AVs), focusing on road lane information derived from aerial imagery. While Earth observation data offers valuable resources for map …
Autonomous VehiclesEarth ObservationPartially Labeled DatasetsSemantic Segmentation+1Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation
Partially-supervised multi-organ medical image segmentation aims to develop a unified semantic segmentation model by utilizing multiple partially-labeled datasets, with each dataset providing labels for a single class of…
Data AugmentationImage SegmentationMedical Image SegmentationPartially Labeled Datasets+1AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but…
Partial Label LearningPartially Labeled DatasetsWeakly-supervised LearningDeep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation
The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the emergence of numerous partially labeled dat…
Organ SegmentationPartially Labeled DatasetsSegmentationReal World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations hav…
Federated LearningPartially Labeled DatasetsPredicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
Fluorescence labeling is the standard approach to reveal cellular structures and other subcellular constituents for microscopy images. However, this invasive procedure may perturb or even kill the cells and the procedure…
Partially Labeled DatasetsFree Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. However, the powerful Mixup sample-mixing data au…
BenchmarkingData Augmentationimage-classificationImage Classification+2Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forge…
class-incremental learningClass Incremental LearningContinual LearningIncremental Learning+2The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data
In modern large-scale observational studies, data collection constraints often result in partially labeled datasets, posing challenges for reliable causal inference, especially due to potential labeling bias and relative…
Causal InferencePartially Labeled DatasetsSelection biasCOSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sam…
Computed Tomography (CT)Medical Image SegmentationOrgan SegmentationPartial Label Learning+3Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in rad…
Organ SegmentationPartially Labeled DatasetsSegmentationTransfer LearningLearning from partially labeled data for multi-organ and tumor segmentation
Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one netwo…
Image SegmentationMedical Image SegmentationPartially Labeled DatasetsSegmentation+3Composite Learning for Robust and Effective Dense Predictions
Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. However, the current practice requires additional labeling efforts for the auxiliary task, while …
Boundary DetectionDepth EstimationMonocular Depth EstimationMulti-Task Learning+2Deep Anomaly Detection and Search via Reinforcement Learning
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers. In this paper, we classify existing semi-supervised AD methods…
Anomaly DetectionEnsemble LearningPartially Labeled Datasetsreinforcement-learning+4Federated Multi-organ Segmentation with Inconsistent Labels
Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However…
DecoderFederated LearningMedical Image AnalysisOrgan Segmentation+1Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity
Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenient to collect multiple small partially l…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOpen-Ended Question AnsweringPartially Labeled DatasetsUniversal Segmentation of 33 Anatomies
In the paper, we present an approach for learning a single model that universally segments 33 anatomical structures, including vertebrae, pelvic bones, and abdominal organs. Our model building has to address the followin…
GPUImage SegmentationMedical Image SegmentationPartially Labeled Datasets+3