Design and Mining of Health Information Systems for Process and Patient Care Improvement

abstract: In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and convenient access to diagnostic images from multiple modalities. How to integrate such HISs and best utilize their data remains a challenging problem due to the disparity of HISs as well as high-dimensionality and heterogeneity of the data. My PhD dissertation research includes three inter-connected and integrated topics and focuses on designing integrated HISs and further developing statistical models and machine learning algorithms for process and patient care improvement. Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients. Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks. Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Gaze transitions when learning with multimedia

Eye tracking methodology is used to examine the influence of interactive multimedia on the allocation of visual attention and its dynamics during learning. We hypothesized that an interactive simulation promotes more organized switching of attention between different elements of multimedia learning material, e.g., textual description and pictorial visualization. Participants studied a description of an algorithm accompanied either by an interactive simulation, self-paced animation, or static illustration. Using a novel framework for entropy-based comparison of gaze transition matrices, results showed that the interactive simulation elicited more careful visual investigation of the learning material as well as reading of the problem description through to its completion

NOGO-A/RTN4A and NOGO-B/RTN4B are simultaneously expressed in epithelial, fibroblast and neuronal cells and maintain ER morphology

Reticulons (RTNs) are a large family of membrane associated proteins with various functions. NOGO-A/RTN4A has a well-known function in limiting neurite outgrowth and restricting the plasticity of the mammalian central nervous system. On the other hand, Reticulon 4 proteins were shown to be involved in forming and maintaining endoplasmic reticulum (ER) tubules. Using comparative transcriptome analysis and qPCR, we show here that NOGO-B/RTN4B and NOGO-A/RTN4A are simultaneously expressed in cultured epithelial, fibroblast and neuronal cells. Electron tomography combined with immunolabelling reveal that both isoforms localize preferably to curved membranes on ER tubules and sheet edges. Morphological analysis of cells with manipulated levels of NOGO-B/RTN4B revealed that it is required for maintenance of normal ER shape; over-expression changes the sheet/tubule balance strongly towards tubules and causes the deformation of the cell shape while depletion of the protein induces formation of large peripheral ER sheets.Peer reviewe

Development and application of molecular and computational tools to image copper in cells

Copper is a trace element which is essential for many biological processes. A deficiency or excess of copper(I) ions, which is its main oxidation state of copper in cellular environment, is increasingly linked to the development of neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease (PD and AD). The regulatory mechanisms for copper(I) are under active investigation and lysosomes which are best known as cellular “incinerators” have been found to play an important role in the trafficking of copper inside the cell. Therefore, it is important to develop reliable experimental methods to detect, monitor and visualise this metal in cells and to develop tools that allow to improve the data quality of microscopy recordings. This would enable the detailed exploration of cellular processes related to copper trafficking through lysosomes. The research presented in this thesis aimed to develop chemical and computational tools that can help to investigate concentration changes of copper(I) in cells (particularly in lysosomes), and it presents a preliminary case study that uses the here developed microscopy image quality enhancement tools to investigate lysosomal mobility changes upon treatment of cells with different PD or AD drugs. Chapter I first reports the synthesis of a previously reported copper(I) probe (CS3). The photophysical properties of this probe and functionality on different cell lines was tested and it was found that this copper(I) sensor predominantly localized in lipid droplets and that its photostability and quantum yield were insufficient to be applied for long term investigations of cellular copper trafficking. Therefore, based on the insights of this probe a new copper(I) selective fluorescent probe (FLCS1) was designed, synthesized, and characterized which showed superior photophysical properties (photostability, quantum yield) over CS3. The probe showed selectivity for copper(I) over other physiological relevant metals and showed strong colocalization in lysosomes in SH-SY5Y cells. This probe was then used to study and monitor lysosomal copper(I) levels via fluorescence lifetime imaging microscopy (FLIM); to the best of my knowledge this is the first copper(I) probe based on emission lifetime. Chapter II explores different computational deep learning approaches for improving the quality of recorded microscopy images. In total two existing networks were tested (fNET, CARE) and four new networks were implemented, tested, and benchmarked for their capabilities of improving the signal-to-noise ratio, upscaling the image size (GMFN, SRFBN-S, Zooming SlowMo) and interpolating image sequences (DAIN, Zooming SlowMo) in z- and t-dimension of multidimensional simulated and real-world datasets. The best performing networks of each category were then tested in combination by sequentially applying them on a low signal-to-noise ratio, low resolution, and low frame-rate image sequence. This image enhancement workstream for investigating lysosomal mobility was established. Additionally, the new frame interpolation networks were implemented in user-friendly Google Colab notebooks and were made publicly available to the scientific community on the ZeroCostDL4Mic platform. Chapter III provides a preliminary case study where the newly developed fluorescent copper(I) probe in combination with the computational enhancement algorithms was used to investigate the effects of five potential Parkinson’s disease drugs (rapamycin, digoxin, curcumin, trehalose, bafilomycin A1) on the mobility of lysosomes in live cells.Open Acces

Fully Unsupervised Image Denoising, Diversity Denoising and Image Segmentation with Limited Annotations

Understanding the processes of cellular development and the interplay of cell shape changes, division and migration requires investigation of developmental processes at the spatial resolution of single cell. Biomedical imaging experiments enable the study of dynamic processes as they occur in living organisms. While biomedical imaging is essential, a key component of exposing unknown biological phenomena is quantitative image analysis. Biomedical images, especially microscopy images, are usually noisy owing to practical limitations such as available photon budget, sample sensitivity, etc. Additionally, microscopy images often contain artefacts due to the optical aberrations in microscopes or due to imperfections in camera sensor and internal electronics. The noisy nature of images as well as the artefacts prohibit accurate downstream analysis such as cell segmentation. Although countless approaches have been proposed for image denoising, artefact removal and segmentation, supervised Deep Learning (DL) based content-aware algorithms are currently the best performing for all these tasks. Supervised DL based methods are plagued by many practical limitations. Supervised denoising and artefact removal algorithms require paired corrupted and high quality images for training. Obtaining such image pairs can be very hard and virtually impossible in most biomedical imaging applications owing to photosensitivity and the dynamic nature of the samples being imaged. Similarly, supervised DL based segmentation methods need copious amounts of annotated data for training, which is often very expensive to obtain. Owing to these restrictions, it is imperative to look beyond supervised methods. The objective of this thesis is to develop novel unsupervised alternatives for image denoising, and artefact removal as well as semisupervised approaches for image segmentation. The first part of this thesis deals with unsupervised image denoising and artefact removal. For unsupervised image denoising task, this thesis first introduces a probabilistic approach for training DL based methods using parametric models of imaging noise. Next, a novel unsupervised diversity denoising framework is presented which addresses the fundamentally non-unique inverse nature of image denoising by generating multiple plausible denoised solutions for any given noisy image. Finally, interesting properties of the diversity denoising methods are presented which make them suitable for unsupervised spatial artefact removal in microscopy and medical imaging applications. In the second part of this thesis, the problem of cell/nucleus segmentation is addressed. The focus is especially on practical scenarios where ground truth annotations for training DL based segmentation methods are scarcely available. Unsupervised denoising is used as an aid to improve segmentation performance in the presence of limited annotations. Several training strategies are presented in this work to leverage the representations learned by unsupervised denoising networks to enable better cell/nucleus segmentation in microscopy data. Apart from DL based segmentation methods, a proof-of-concept is introduced which views cell/nucleus segmentation from the perspective of solving a label fusion problem. This method, through limited human interaction, learns to choose the best possible segmentation for each cell/nucleus using only a pool of diverse (and possibly faulty) segmentation hypotheses as input. In summary, this thesis seeks to introduce new unsupervised denoising and artefact removal methods as well as semi-supervised segmentation methods which can be easily deployed to directly and immediately benefit biomedical practitioners with their research

Heartwave biometric authentication using machine learning algorithms

PhD ThesisThe advancement of IoT, cloud services and technologies have prompted heighten IT access security. Many products and solutions have implemented biometric solution to address the security concern. Heartwave as biometric mode offers the potential due to the inability to falsify the signal and ease of signal acquisition from fingers. However the highly variated heartrate signal, due to heartrate has imposed much headwinds in the development of heartwave based biometric authentications. The thesis first review the state-of-the-arts in the domains of heartwave segmentation and feature extraction, and identifying discriminating features and classifications. In particular this thesis proposed a methodology of Discrete Wavelet Transformation integrated with heartrate dependent parameters to extract discriminating features reliably and accurately. In addition, statistical methodology using Gaussian Mixture Model-Hidden Markov Model integrated with user specific threshold and heartrate have been proposed and developed to provide classification of individual under varying heartrates. This investigation has led to the understanding that individual discriminating feature is a variable against heartrate. Similarly, the neural network based methodology leverages on ensemble-Deep Belief Network (DBN) with stacked DBN coded using Multiview Spectral Embedding has been explored and achieved good performance in classification. Importantly, the amount of data required for training is significantly reduce

DEEP NEURAL NETWORKS AND REGRESSION MODELS FOR OBJECT DETECTION AND POSE ESTIMATION

Estimating the pose, orientation and the location of objects has been a central problem addressed by the computer vision community for decades. In this dissertation, we propose new approaches for these important problems using deep neural networks as well as tree-based regression models. For the first topic, we look at the human body pose estimation problem and propose a novel regression-based approach. The goal of human body pose estimation is to predict the locations of body joints, given an image of a person. Due to significant variations introduced by pose, clothing and body styles, it is extremely difficult to address this task by a standard application of the regression method. Thus, we address this task by dividing the whole body pose estimation problem into a set of local pose estimation problems by introducing a dependency graph which describes the dependency among different body joints. For each local pose estimation problem, we train a boosted regression tree model and estimate the pose by progressively applying the regression along the paths in a dependency graph starting from the root node. Our next work is on improving the traditional regression tree method and demonstrate its effectiveness for pose/orientation estimation tasks. The main issues of the traditional regression training are, 1) the node splitting is limited to binary splitting, 2) the form of the splitting function is limited to thresholding on a single dimension of the input vector and 3) the best splitting function is found by exhaustive search. We propose a novel node splitting algorithm for regression tree training which does not have the issues mentioned above. The algorithm proceeds by first applying k-means clustering in the output space, conducting multi-class classification by support vector machine (SVM) and determining the constant estimate at each leaf node. We apply the regression forest that includes our regression tree models to head pose estimation, car orientation estimation and pedestrian orientation estimation tasks and demonstrate its superiority over various standard regression methods. Next, we turn our attention to the role of pose information for the object detection task. In particular, we focus on the detection of fashion items a person is wearing or carrying. It is clear that the locations of these items are strongly correlated with the pose of the person. To address this task, we first generate a set of candidate bounding boxes by using an object proposal algorithm. For each candidate bounding box, image features are extracted by a deep convolutional neural network pre-trained on a large image dataset and the detection scores are generated by SVMs. We introduce a pose-dependent prior on the geometry of the bounding boxes and combine it with the SVM scores. We demonstrate that the proposed algorithm achieves significant improvement in the detection performance. Lastly, we address the object detection task by exploring a way to incorporate an attention mechanism into the detection algorithm. Humans have the capability of allocating multiple fixation points, each of which attends to different locations and scales of the scene. However, such a mechanism is missing in the current state-of-the-art object detection methods. Inspired by the human vision system, we propose a novel deep network architecture that imitates this attention mechanism. For detecting objects in an image, the network adaptively places a sequence of glimpses at different locations in the image. Evidences of the presence of an object and its location are extracted from these glimpses, which are then fused for estimating the object class and bounding box coordinates. Due to the lack of ground truth annotations for the visual attention mechanism, we train our network using a reinforcement learning algorithm. Experiment results on standard object detection benchmarks show that the proposed network consistently outperforms the baseline networks that do not employ the attention mechanism