Dimensionality reduction (DR) is an efficient approach to reduce the size of
data by capturing the informative intrinsic features and discarding the noise. DR
methods can be grouped through a variety of categories, e.g. supervised/ unsupervised,
linear/non-linear or parametric/non-parametric. Objective function based
methods can be grouped into convex and non...
In histopathological image analysis, image classification as well as pattern detection play a crucial role in the diagnosis and treatment process since the goal is to not only differentiate cancer types but also identify cancerous manifestations. Fully supervised learning strategies tend to address these problems using manually annotated cancerous regions...
The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...