2,070 research outputs found
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
Physics-Informed Computer Vision: A Review and Perspectives
Incorporation of physical information in machine learning frameworks are
opening and transforming many application domains. Here the learning process is
augmented through the induction of fundamental knowledge and governing physical
laws. In this work we explore their utility for computer vision tasks in
interpreting and understanding visual data. We present a systematic literature
review of formulation and approaches to computer vision tasks guided by
physical laws. We begin by decomposing the popular computer vision pipeline
into a taxonomy of stages and investigate approaches to incorporate governing
physical equations in each stage. Existing approaches in each task are analyzed
with regard to what governing physical processes are modeled, formulated and
how they are incorporated, i.e. modify data (observation bias), modify networks
(inductive bias), and modify losses (learning bias). The taxonomy offers a
unified view of the application of the physics-informed capability,
highlighting where physics-informed learning has been conducted and where the
gaps and opportunities are. Finally, we highlight open problems and challenges
to inform future research. While still in its early days, the study of
physics-informed computer vision has the promise to develop better computer
vision models that can improve physical plausibility, accuracy, data efficiency
and generalization in increasingly realistic applications
Mathematics of biomedical imaging today—a perspective
Biomedical imaging is a fascinating, rich and dynamic research area, which has huge importance in biomedical research and clinical practice alike. The key technology behind the processing, and automated analysis and quantification of imaging data is mathematics. Starting with the optimisation of the image acquisition and the reconstruction of an image from indirect tomographic measurement data, all the way to the automated segmentation of tumours in medical images and the design of optimal treatment plans based on image biomarkers, mathematics appears in all of these in different flavours. Non-smooth optimisation in the context of sparsity-promoting image priors, partial differential equations for image registration and motion estimation, and deep neural networks for image segmentation, to name just a few. In this article, we present and review mathematical topics that arise within the whole biomedical imaging pipeline, from tomographic measurements to clinical support tools, and highlight some modern topics and open problems. The article is addressed to both biomedical researchers who want to get a taste of where mathematics arises in biomedical imaging as well as mathematicians who are interested in what mathematical challenges biomedical imaging research entails
Integrating Deep Learning into Digital Rock Analysis Workflow
Digital Rock Analysis (DRA) has expanded our knowledge about natural phenomena in various geoscience specialties. DRA as an emerging technology has limitations including (1) the trade-off between the size of spatial domain and resolution, (2) methodological and human-induced errors in segmentation, and (3) the computational costs associated with intensive modeling. Deep learning (DL) methods are utilized to alleviate these limitations.
First, two DL frameworks are utilized to probe the performance gains from using Convolutional Neural Networks (CNN) to super-resolve and segment real multi-resolution X-ray images of complex carbonate rocks. The first framework experiments the applications of U-Net and U-ResNet architectures to obtain macropore, solid, and micropore segmented images in an end-to-end scheme. The second framework segregates the super-resolution and segmentation into two networks: EDSR and U-ResNet. Both frameworks show consistent performance indicated by the voxel-wise accuracy metrics, the measured phase morphology, and flow characteristics. The end-to-end frameworks are shown to be superior to using a segregated approach confirming the adequacy of end-to-end learning for performing complex tasks.
Second, CNNs accuracy margins in estimating physical properties of porous media 2d X-ray images are investigated. Binary and greyscale sandstone images are used as an input to CNNs architectures to estimate porosity, specific surface area, and average pore size of three sandstone images. The results show encouraging margins of accuracy where the error in estimating these properties can be up to 6% when using binary images and up to 7% when using greyscale images.
Third, the suitability of CNNs as regression tools to predict a more challenging property, permeability, is investigated. Two complex CNNs architectures (ResNet and ResNext) are applied to learn the morphology of pore space in 3D porous media images for flow-based characterization. The dataset includes more than 29,000 3d subvolumes of multiple sandstone and carbonates rocks. The findings show promising regression accuracy using binary images. Accuracy gains are observed using conductivity maps as an input to the networks. Permeability inference on unseen samples can be achieved in 120 ms/sample with an average relative error of 18.9%.
This thesis demonstrates the significant potential of deep learning in improving DRA capabilities
Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks
Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements
Deep learning-based diagnostic system for malignant liver detection
Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most
common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent,
accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification.
In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms.
However, such traditional methods could immensely affect the structural properties of processed images with
inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use.
To address these limitations, I propose novel methodologies in this dissertation. First, I modified a
generative adversarial network to perform deblurring and contrast adjustment on computed tomography
(CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise
segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network
to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver
detection.
The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods.
The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification.
A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second
method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized
to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis
performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from
abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions.
Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants.
In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore,
the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis
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