4,446 research outputs found
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
The crucial components of a conventional image registration method are the
choice of the right feature representations and similarity measures. These two
components, although elaborately designed, are somewhat handcrafted using human
knowledge. To this end, these two components are tackled in an end-to-end
manner via reinforcement learning in this work. Specifically, an artificial
agent, which is composed of a combined policy and value network, is trained to
adjust the moving image toward the right direction. We train this network using
an asynchronous reinforcement learning algorithm, where a customized reward
function is also leveraged to encourage robust image registration. This trained
network is further incorporated with a lookahead inference to improve the
registration capability. The advantage of this algorithm is fully demonstrated
by our superior performance on clinical MR and CT image pairs to other
state-of-the-art medical image registration methods
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
Deep learning belongs to the field of artificial intelligence, where machines
perform tasks that typically require some kind of human intelligence. Similar
to the basic structure of a brain, a deep learning algorithm consists of an
artificial neural network, which resembles the biological brain structure.
Mimicking the learning process of humans with their senses, deep learning
networks are fed with (sensory) data, like texts, images, videos or sounds.
These networks outperform the state-of-the-art methods in different tasks and,
because of this, the whole field saw an exponential growth during the last
years. This growth resulted in way over 10,000 publications per year in the
last years. For example, the search engine PubMed alone, which covers only a
sub-set of all publications in the medical field, provides already over 11,000
results in Q3 2020 for the search term 'deep learning', and around 90% of these
results are from the last three years. Consequently, a complete overview over
the field of deep learning is already impossible to obtain and, in the near
future, it will potentially become difficult to obtain an overview over a
subfield. However, there are several review articles about deep learning, which
are focused on specific scientific fields or applications, for example deep
learning advances in computer vision or in specific tasks like object
detection. With these surveys as a foundation, the aim of this contribution is
to provide a first high-level, categorized meta-survey of selected reviews on
deep learning across different scientific disciplines. The categories (computer
vision, language processing, medical informatics and additional works) have
been chosen according to the underlying data sources (image, language, medical,
mixed). In addition, we review the common architectures, methods, pros, cons,
evaluations, challenges and future directions for every sub-category.Comment: 83 pages, 22 figures, 9 tables, 100 reference
Deep Object-Centric Representations for Generalizable Robot Learning
Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning
Meta-Learning Initializations for Interactive Medical Image Registration
We present a meta-learning framework for interactive medical image
registration. Our proposed framework comprises three components: a
learning-based medical image registration algorithm, a form of user interaction
that refines registration at inference, and a meta-learning protocol that
learns a rapidly adaptable network initialization. This paper describes a
specific algorithm that implements the registration, interaction and
meta-learning protocol for our exemplar clinical application: registration of
magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled
transrectal ultrasound (TRUS) images. Our approach obtains comparable
registration error (4.26 mm) to the best-performing non-interactive
learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the
data, and occurring in real-time during acquisition. Applying sparsely sampled
data to non-interactive methods yields higher registration errors (6.26 mm),
demonstrating the effectiveness of interactive MR-TRUS registration, which may
be applied intraoperatively given the real-time nature of the adaptation
process.Comment: 11 pages, 10 figures. Paper accepted to IEEE Transactions on Medical
Imaging (October 26 2022
- …