Deep semi-supervised learning for brain tumor classification
نویسندگان
چکیده
منابع مشابه
Learning a Deep Hybrid Model for Semi-Supervised Text Classification
We present a novel fine-tuning algorithm in a deep hybrid architecture for semisupervised text classification. During each increment of the online learning process, the fine-tuning algorithm serves as a top-down mechanism for pseudo-jointly modifying model parameters following a bottom-up generative learning pass. The resulting model, trained under what we call the Bottom-Up-Top-Down learning a...
متن کاملSemi-Supervised Classification for Intracortical Brain-Computer Interfaces Semi-Supervised Classification for Intracortical Brain-Computer Interfaces
Intracortical brain-computer interface (BCI) systems may one day allow paralyzed patients to interface with robotic arms or computer programs using their thoughts alone. However, a common and unaddressed issue with these systems is that due to small instabilities in the recorded signals, the decoding algorithms they rely upon must be retrained daily in a supervised manner. While this may be acc...
متن کاملSemi-supervised deep kernel learning
Deep learning techniques have led to massive improvements in recent years, but large amounts of labeled data are typically required to learn these complex models. We present a semi-supervised approach for training deep models that combines the feature learning capabilities of neural networks with the probabilistic modeling of Gaussian processes and demonstrate that unlabeled data can significan...
متن کاملSemi-Supervised Learning for Blog Classification
Blog classification (e.g., identifying bloggers’ gender or age) is one of the most interesting current problems in blog analysis. Although this problem is usually solved by applying supervised learning techniques, the large labeled dataset required for training is not always available. In contrast, unlabeled blogs can easily be collected from the web. Therefore, a semi-supervised learning metho...
متن کاملSemi-supervised learning for image classification
Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10, 000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling proce...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Medical Imaging
سال: 2020
ISSN: 1471-2342
DOI: 10.1186/s12880-020-00485-0