In this study we developed a graph based semi-supervised learning (SSL) scheme using
deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a …

Completely labeled pathology datasets are often challenging and time-consuming to obtain.
Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points …

摘要半监督学习研究如何同时利用有类标签的样本和无类标签的样例改进学习性能,
成为近年来机器学习领域的研究热点. 鉴于半监督学习的理论意义和实际应用价值 …

Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical
scenario where out-of-distribution (OOD) samples are contained in the unlabeled data …

Objective Many machine learning models have aided medical specialists in diagnosis and
prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the …

Semi-supervised learning is attracting increasing attention due to the fact that datasets of
many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has …

Background Prognostic studies of breast cancer survivability have been aided by machine
learning algorithms, which can predict the survival of a particular patient based on historical …

Semi-supervised node classification on graphs is an important research problem, with many
real-world applications in information retrieval such as content classification on a social …

While node semantics have been extensively explored in social networks, little research
attention has been paid to pro le edge semantics, ie, social relations. Ideal edge semantics …

The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled
training data required by learning from both labeled and unlabeled instances. Macskassy …