Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge.
Jun MaYao ZhangSong GuCheng GeShihao MaAdamo YoungCheng ZhuKangkang MengXin YangZiyan HuangFan ZhangWentao LiuYuanKe PanShoujin HuangJiacheng WangMingze SunWeixin XuDengqiang JiaJae Won ChoiNatália AlvesBram De WildeGregor KoehlerYajun WuManuel WiesenfarthQiongjie ZhuGuoqiang DongJian Hethe FLARE Challenge ConsortiumBo WangPublished in: CoRR (2023)
Keyphrases
- unlabeled data
- labeled data
- semi supervised learning
- semi supervised
- abdominal ct images
- co training
- active learning
- semi supervised classification
- lymph nodes
- training data
- text classification
- supervised learning
- learning algorithm
- data points
- labeled examples
- labeled and unlabeled data
- labeled training data
- number of labeled examples
- text categorization
- class labels
- training set
- multi view learning
- small set of labeled
- training examples
- label propagation
- medical images
- data sets
- supervised learning algorithms
- domain adaptation
- data mining
- labeled data for training
- prior knowledge
- transfer learning
- naive bayes
- pairwise
- object recognition
- unlabeled instances
- semisupervised learning
- reinforcement learning
- supervised and semi supervised
- machine learning
- positive examples