In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation an...
Yuanjie Zheng, Karl Steiner, Thomas Bauer, Jingyi ...
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
Data is often collected over a distributed network, but in many cases, is so voluminous that it is impractical and undesirable to collect it in a central location. Instead, we mus...
At present time the Internet has become a major source of information and a powerful didactic tool. Furthermore, the development of digital equipment, allows to acquire and store ...
Abdelaziz Bensrhair, Alexandrina Rogozan, Eugen Ba...
We propose a competitive finite mixture of neurons (or perceptrons) for solving binary classification problems. Our classifier includes a prior for the weights between different n...
Karthik Sridharan, Matthew J. Beal, Venu Govindara...