Partially Labeled Datasets
10 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Partially Labeled Datasets
Most implemented papers
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
cardio-ai/fed-foundation-model-cardiac-ct
•
•
First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads.
Multi-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.
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations).
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
jianpengz/DoDNet
•
•
CVPR 2021
To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.
Federated Multi-organ Segmentation with Inconsistent Labels
Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods.
Learning from partially labeled data for multi-organ and tumor segmentation
jianpengz/DoDNet
•
•
To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets.
Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
grypesc/camp
•
•
Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories.
Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
Recently, in silico labeling has emerged as a promising alternative, aiming to use machine learning models to directly predict the fluorescently labeled images from label-free microscopy.
AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
libeibeics/asyco
•
•
Specifically, the disambiguation network is trained with self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence.
Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation
xjiangmed/ltuda
•
•
However, the limited availability of labeled foreground organs and the absence of supervision to distinguish unlabeled foreground organs from the background pose a significant challenge, which leads to a distribution mismatch between labeled and unlabeled pixels.
