591 research outputs found
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
Recent advances in deep learning have achieved promising results on many
biomedical image segmentation benchmarks. However, due to large variations in
biomedical images (different modalities, image settings, objects, noise, etc),
to utilize deep learning on a new application, it usually needs a new set of
training data. This can incur a great deal of annotation effort and cost,
because only biomedical experts can annotate effectively, and often there are
too many instances in images (e.g., cells) to annotate. In this paper, we aim
to address the following question: With limited effort (e.g., time) for
annotation, what instances should be annotated in order to attain the best
performance? We present a deep active learning framework that combines fully
convolutional network (FCN) and active learning to significantly reduce
annotation effort by making judicious suggestions on the most effective
annotation areas. We utilize uncertainty and similarity information provided by
FCN and formulate a generalized version of the maximum set cover problem to
determine the most representative and uncertain areas for annotation. Extensive
experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node
ultrasound image segmentation dataset show that, using annotation suggestions
by our method, state-of-the-art segmentation performance can be achieved by
using only 50% of training data.Comment: Accepted at MICCAI 201
Submodularity of Energy Related Controllability Metrics
The quantification of controllability and observability has recently received
new interest in the context of large, complex networks of dynamical systems. A
fundamental but computationally difficult problem is the placement or selection
of actuators and sensors that optimize real-valued controllability and
observability metrics of the network. We show that several classes of energy
related metrics associated with the controllability Gramian in linear dynamical
systems have a strong structural property, called submodularity. This property
allows for an approximation guarantee by using a simple greedy heuristic for
their maximization. The results are illustrated for randomly generated systems
and for placement of power electronic actuators in a model of the European
power grid.Comment: 7 pages, 2 figures; submitted to the 2014 IEEE Conference on Decision
and Contro
- …