331 research outputs found
Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation
Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications,
including image classification, game playing, protein structure analysis,
language translation, and content generation. Despite these powerful
applications, there are still many tasks in our daily life that are rather
simple to humans but pose great challenges to AI. These include image and
language understanding, few-shot learning, abstract concepts, and low-energy
cost computing. Thus, learning from the brain is still a promising way that can
shed light on the development of next-generation AI. The brain is arguably the
only known intelligent machine in the universe, which is the product of
evolution for animals surviving in the natural environment. At the behavior
level, psychology and cognitive sciences have demonstrated that human and
animal brains can execute very intelligent high-level cognitive functions. At
the structure level, cognitive and computational neurosciences have unveiled
that the brain has extremely complicated but elegant network forms to support
its functions. Over years, people are gathering knowledge about the structure
and functions of the brain, and this process is accelerating recently along
with the initiation of giant brain projects worldwide. Here, we argue that the
general principles of brain functions are the most valuable things to inspire
the development of AI. These general principles are the standard rules of the
brain extracting, representing, manipulating, and retrieving information, and
here we call them the first principles of the brain. This paper collects six
such first principles. They are attractor network, criticality, random network,
sparse coding, relational memory, and perceptual learning. On each topic, we
review its biological background, fundamental property, potential application
to AI, and future development.Comment: 59 pages, 5 figures, review articl
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing
Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts
Representation learning for uncertainty-aware clinical decision support
Over the last decade, there has been an increasing trend towards digitalization in healthcare, where a growing amount of patient data is collected and stored electronically. These recorded data are known as electronic health records. They are the basis for state-of-the-art research on clinical decision support so that better patient care can be delivered with the help of advanced analytical techniques like machine learning. Among various technical fields in machine learning, representation learning is about learning good representations from raw data to extract useful information for downstream prediction tasks. Deep learning, a crucial class of methods in representation learning, has achieved great success in many fields such as computer vision and natural language processing. These technical breakthroughs would presumably further advance the research and development of data analytics in healthcare. This thesis addresses clinically relevant research questions by developing algorithms based on state-of-the-art representation learning techniques. When a patient visits the hospital, a physician will suggest a treatment in a deterministic manner. Meanwhile, uncertainty comes into play when the past statistics of treatment decisions from various physicians are analyzed, as they would possibly suggest different treatments, depending on their training and experiences. The uncertainty in clinical decision-making processes is the focus of this thesis. The models developed for supporting these processes will therefore have a probabilistic nature. More specifically, the predictions are predictive distributions in regression tasks and probability distributions over, e.g., different treatment decisions, in classification tasks. The first part of the thesis is concerned with prescriptive analytics to provide treatment recommendations. Apart from patient information and treatment decisions, the outcome after the respective treatment is included in learning treatment suggestions. The problem setting is known as learning individualized treatment rules and is formulated as a contextual bandit problem. A general framework for learning individualized treatment rules using data from observational studies is presented based on state-of-the-art representation learning techniques. From various offline evaluation methods, it is shown that the treatment policy in our proposed framework can demonstrate better performance than both physicians and competitive baselines. Subsequently, the uncertainty-aware regression models in diagnostic and predictive analytics are studied. Uncertainty-aware deep kernel learning models are proposed, which allow the estimation of the predictive uncertainty by a pipeline of neural networks and a sparse Gaussian process. By considering the input data structure, respective models are developed for diagnostic medical image data and sequential electronic health records. Various pre-training methods from representation learning are adapted to investigate their impacts on the proposed models. Through extensive experiments, it is shown that the proposed models delivered better performance than common architectures in most cases. More importantly, uncertainty-awareness of the proposed models is illustrated by systematically expressing higher confidence in more accurate predictions and less confidence in less accurate ones. The last part of the thesis is about missing data imputation in descriptive analytics, which provides essential evidence for subsequent decision-making processes. Rather than traditional mean and median imputation, a more advanced solution based on generative adversarial networks is proposed. The presented method takes the categorical nature of patient features into consideration, which enables the stabilization of the adversarial training. It is shown that the proposed method can better improve the predictive accuracy compared to traditional imputation baselines
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