319 research outputs found
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Deep Learning of Representations: Looking Forward
Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges
Exploring variability in medical imaging
Although recent successes of deep learning and novel machine learning techniques improved the perfor-
mance of classification and (anomaly) detection in computer vision problems, the application of these
methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this
is the amount of variability that is encountered and encapsulated in human anatomy and subsequently
reflected in medical images. This fundamental factor impacts most stages in modern medical imaging
processing pipelines.
Variability of human anatomy makes it virtually impossible to build large datasets for each disease
with labels and annotation for fully supervised machine learning. An efficient way to cope with this is
to try and learn only from normal samples. Such data is much easier to collect. A case study of such
an automatic anomaly detection system based on normative learning is presented in this work. We
present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative
models, which are trained only utilising normal/healthy subjects.
However, despite the significant improvement in automatic abnormality detection systems, clinical
routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis
and localise abnormalities. Integrating human expert knowledge into the medical imaging processing
pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per-
spective of building an automated medical imaging system, it is still an open issue, to what extent
this kind of variability and the resulting uncertainty are introduced during the training of a model
and how it affects the final performance of the task. Consequently, it is very important to explore the
effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as
on the model’s performance in a specific machine learning task. A thorough investigation of this issue
is presented in this work by leveraging automated estimates for machine learning model uncertainty,
inter-observer variability and segmentation task performance in lung CT scan images.
Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging
was attempted. This state-of-the-art survey includes both conventional pattern recognition methods
and deep learning based methods. It is one of the first literature surveys attempted in the specific
research area.Open Acces
Deep learning for time series classification
Time series analysis is a field of data science which is interested in
analyzing sequences of numerical values ordered in time. Time series are
particularly interesting because they allow us to visualize and understand the
evolution of a process over time. Their analysis can reveal trends,
relationships and similarities across the data. There exists numerous fields
containing data in the form of time series: health care (electrocardiogram,
blood sugar, etc.), activity recognition, remote sensing, finance (stock market
price), industry (sensors), etc. Time series classification consists of
constructing algorithms dedicated to automatically label time series data. The
sequential aspect of time series data requires the development of algorithms
that are able to harness this temporal property, thus making the existing
off-the-shelf machine learning models for traditional tabular data suboptimal
for solving the underlying task. In this context, deep learning has emerged in
recent years as one of the most effective methods for tackling the supervised
classification task, particularly in the field of computer vision. The main
objective of this thesis was to study and develop deep neural networks
specifically constructed for the classification of time series data. We thus
carried out the first large scale experimental study allowing us to compare the
existing deep methods and to position them compared other non-deep learning
based state-of-the-art methods. Subsequently, we made numerous contributions in
this area, notably in the context of transfer learning, data augmentation,
ensembling and adversarial attacks. Finally, we have also proposed a novel
architecture, based on the famous Inception network (Google), which ranks among
the most efficient to date.Comment: PhD thesi
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy
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