Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
[HTML][HTML] Deep learning in diverse intelligent sensor based systems
Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …
virtually all fields of science and engineering. The increasing complexity and the large …
Multiple instance learning: A survey of problem characteristics and applications
Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …
Towards a neural statistician
An efficient learner is one who reuses what they already know to tackle a new problem. For
a machine learner, this means understanding the similarities amongst datasets. In order to …
a machine learner, this means understanding the similarities amongst datasets. In order to …
From group to individual labels using deep features
In many classification problems labels are relatively scarce. One context in which this occurs
is where we have labels for groups of instances but not for the instances themselves, as in …
is where we have labels for groups of instances but not for the instances themselves, as in …
[图书][B] Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods
S Vluymans - 2019 - Springer
This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …
Variational learning on aggregate outputs with Gaussian processes
While a typical supervised learning framework assumes that the inputs and the outputs are
measured at the same levels of granularity, many applications, including global mapping of …
measured at the same levels of granularity, many applications, including global mapping of …
[HTML][HTML] Model-based learning for point pattern data
This article proposes a framework for model-based point pattern learning using point
process theory. Likelihood functions for point pattern data derived from point process theory …
process theory. Likelihood functions for point pattern data derived from point process theory …
[HTML][HTML] 3D shape modeling for cell nuclear morphological analysis and classification
AA Kalinin, A Allyn-Feuer, A Ade, GV Fon, W Meixner… - Scientific reports, 2018 - nature.com
Quantitative analysis of morphological changes in a cell nucleus is important for the
understanding of nuclear architecture and its relationship with pathological conditions such …
understanding of nuclear architecture and its relationship with pathological conditions such …
Distant-supervision of heterogeneous multitask learning for social event forecasting with multilingual indicators
Open-source indicators such as social media can be very effective precursors for forecasting
future societal events. As events are often preceded by social indicators generated by …
future societal events. As events are often preceded by social indicators generated by …