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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works
Leveraging Image-based Generative Adversarial Networks for Time Series Generation
Generative models for images have gained significant attention in computer
vision and natural language processing due to their ability to generate
realistic samples from complex data distributions. To leverage the advances of
image-based generative models for the time series domain, we propose a
two-dimensional image representation for time series, the Extended
Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time
series dynamics in a scale-invariant and invertible way, reducing training time
and improving sample quality. We benchmark synthetic XIRPs obtained by an
off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image
representations and models regarding similarity and predictive ability metrics.
Our novel, validated image representation for time series consistently and
significantly outperforms a state-of-the-art RNN-based generative model
regarding predictive ability. Further, we introduce an improved stochastic
inversion to substantially improve simulation quality regardless of the
representation and provide the prospect of transfer potentials in other
domains
Controlled time series generation for automotive software-in-the-loop testing using GANs
Testing automotive mechatronic systems partly uses the software-in-the-loop
approach, where systematically covering inputs of the system-under-test remains
a major challenge. In current practice, there are two major techniques of input
stimulation. One approach is to craft input sequences which eases control and
feedback of the test process but falls short of exposing the system to
realistic scenarios. The other is to replay sequences recorded from field
operations which accounts for reality but requires collecting a well-labeled
dataset of sufficient capacity for widespread use, which is expensive. This
work applies the well-known unsupervised learning framework of Generative
Adversarial Networks (GAN) to learn an unlabeled dataset of recorded in-vehicle
signals and uses it for generation of synthetic input stimuli. Additionally, a
metric-based linear interpolation algorithm is demonstrated, which guarantees
that generated stimuli follow a customizable similarity relationship with
specified references. This combination of techniques enables controlled
generation of a rich range of meaningful and realistic input patterns,
improving virtual test coverage and reducing the need for expensive field
tests.Comment: Preprint of paper accepted at The Second IEEE International
Conference on Artificial Intelligence Testing, April 13-16, 2020, Oxford, U
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably their most significant impact has been in the area of
computer vision where great advances have been made in challenges such as
plausible image generation, image-to-image translation, facial attribute
manipulation and similar domains. Despite the significant successes achieved to
date, applying GANs to real-world problems still poses significant challenges,
three of which we focus on here. These are: (1) the generation of high quality
images, (2) diversity of image generation, and (3) stable training. Focusing on
the degree to which popular GAN technologies have made progress against these
challenges, we provide a detailed review of the state of the art in GAN-related
research in the published scientific literature. We further structure this
review through a convenient taxonomy we have adopted based on variations in GAN
architectures and loss functions. While several reviews for GANs have been
presented to date, none have considered the status of this field based on their
progress towards addressing practical challenges relevant to computer vision.
Accordingly, we review and critically discuss the most popular
architecture-variant, and loss-variant GANs, for tackling these challenges. Our
objective is to provide an overview as well as a critical analysis of the
status of GAN research in terms of relevant progress towards important computer
vision application requirements. As we do this we also discuss the most
compelling applications in computer vision in which GANs have demonstrated
considerable success along with some suggestions for future research
directions. Code related to GAN-variants studied in this work is summarized on
https://github.com/sheqi/GAN_Review.Comment: Accepted by ACM Computing Surveys, 23 November 202
30 Years of Synthetic Data
The idea to generate synthetic data as a tool for broadening access to
sensitive microdata has been proposed for the first time three decades ago.
While first applications of the idea emerged around the turn of the century,
the approach really gained momentum over the last ten years, stimulated at
least in parts by some recent developments in computer science. We consider the
upcoming 30th jubilee of Rubin's seminal paper on synthetic data (Rubin, 1993)
as an opportunity to look back at the historical developments, but also to
offer a review of the diverse approaches and methodological underpinnings
proposed over the years. We will also discuss the various strategies that have
been suggested to measure the utility and remaining risk of disclosure of the
generated data.Comment: 42 page
Generative models for music using transformer architectures
openThis thesis focus on growth and impact of Transformes architectures which are mainly used for Natural Language Processing tasks for Audio generation. We think that music, with its notes, chords, and volumes, is a language. You could think of symbolic representation of music as human language.
A brief sound synthesis history which gives basic foundation for modern AI-generated music models is mentioned . The most recent in AI-generated audio is carefully studied and instances of AI-generated music is told in many contexts. Deep learning models and their applications to real-world issues are one of the key subjects that are covered.
The main areas of interest include transformer-based audio generation, including the training procedure, encoding and decoding techniques, and post-processing stages. Transformers have several key advantages, including long-term consistency and the ability to create minute-long audio compositions.
Numerous studies on the various representations of music have been explained, including how neural network and deep learning techniques can be used to apply symbolic melodies, musical arrangements, style transfer, and sound production.
This thesis largely focuses on transformation models, but it also recognises the importance of numerous AI-based generative models, including GAN.
Overall, this thesis enhances generative models for music composition and provides a complete understanding of transformer design. It shows the possibilities of AI-generated sound synthesis by emphasising the most current developments.This thesis focus on growth and impact of Transformes architectures which are mainly used for Natural Language Processing tasks for Audio generation. We think that music, with its notes, chords, and volumes, is a language. You could think of symbolic representation of music as human language.
A brief sound synthesis history which gives basic foundation for modern AI-generated music models is mentioned . The most recent in AI-generated audio is carefully studied and instances of AI-generated music is told in many contexts. Deep learning models and their applications to real-world issues are one of the key subjects that are covered.
The main areas of interest include transformer-based audio generation, including the training procedure, encoding and decoding techniques, and post-processing stages. Transformers have several key advantages, including long-term consistency and the ability to create minute-long audio compositions.
Numerous studies on the various representations of music have been explained, including how neural network and deep learning techniques can be used to apply symbolic melodies, musical arrangements, style transfer, and sound production.
This thesis largely focuses on transformation models, but it also recognises the importance of numerous AI-based generative models, including GAN.
Overall, this thesis enhances generative models for music composition and provides a complete understanding of transformer design. It shows the possibilities of AI-generated sound synthesis by emphasising the most current developments
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