344 research outputs found
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for
studying brain activity. During an fMRI session, the subject executes a set of
tasks (task-related fMRI study) or no tasks (resting-state fMRI), and a
sequence of 3-D brain images is obtained for further analysis. In the course of
fMRI, some sources of activation are caused by noise and artifacts. The removal
of these sources is essential before the analysis of the brain activations.
Deep Neural Network (DNN) architectures can be used for denoising and artifact
removal. The main advantage of DNN models is the automatic learning of abstract
and meaningful features, given the raw data. This work presents advanced DNN
architectures for noise and artifact classification, using both spatial and
temporal information in resting-state fMRI sessions. The highest performance is
achieved by a voting schema using information from all the domains, with an
average accuracy of over 98% and a very good balance between the metrics of
sensitivity and specificity (98.5% and 97.5% respectively).Comment: Under Review, ICASSP 202
Image Diversification via Deep Learning based Generative Models
Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases.
To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications.
Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets.
Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for research for the
past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The
modern advent of big data and the development of data-oriented fields like deep learning have provided advancements
in food category recognition. With increasing computational power and ever-larger food datasets,
the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied
to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We
survey the core components for constructing a machine learning system for food category recognition, including
datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a
particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer
learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments
in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial
Fund (RP202G0289)LIAS (P202ED10Data Science
Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK
Education Fund (OP202006)BBSRC (RM32G0178B8
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