29,021 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics applications exhibit these conditions and thus
facilitate the potential to incrementally adapt a learnt model over minor
shifts which integrate to massive differences over time. Our work presents an
adversarial approach for lifelong, incremental domain adaptation which benefits
from unsupervised alignment to a series of intermediate domains which
successively diverge from the labelled source domain. We empirically
demonstrate that our incremental approach improves handling of large appearance
changes, e.g. day to night, on a traversable-path segmentation task compared
with a direct, single alignment step approach. Furthermore, by approximating
the feature distribution for the source domain with a generative adversarial
network, the deployment module can be rendered fully independent of retaining
potentially large amounts of the related source training data for only a minor
reduction in performance.Comment: International Conference on Robotics and Automation 201
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