딥러닝 기반 생존분석이 적용된 심혈관질환 위험 평가 모델 성능 향상을 위한 콕스 모형과 결합된 하이브리드 접근법: 헬스케어-환경 연계 데이터 활용 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 박상민 .Background and aims: The contribution of different cardiovascular disease (CVD) risk factors for the risk evaluation and predictive modeling for incident CVD is often debated. Also, to what extent data on CVD risk factors from multiple data categories should be collected for comprehensive risk assessment and predictive modeling for CVD risk using survival analysis is uncertain despite the increasing availability of the relevant data sources. This study aimed to evaluate the contribution of different data categories derived from integrated data on healthcare and environmental exposure to the risk evaluation and prediction models for CVD risk using deep learning based survival analysis in combination with Cox proportional hazards regression and Cox proportional hazards regression. Methods: Information on the comprehensive list of CVD risk factors were collected from systematic reviews of variables included in the conventional CVD risk assessment tools and observational studies from medical literature database (PubMed and Embase). Each risk factor was screened for availability in the National Health Insurance Service-National Sample Cohort (NHIS-NSC) linked to environmental exposure data on cumulative particulate matter and urban green space using residential area code. Individual records of 137,249 patients more than 40 years of age who underwent the biennial national health screening between 2009 and 2010 without previous history of CVD were followed up for incident CVD event from January 1, 2011 to December 31, 2013 in the NHIS-NSC with data linkage to environmental exposure. Statistics-based variable selection methods were implemented as follows: statistical significance, subset with the minimum (best) Akaike Information Criteria (AIC), variables selected from the regularized Cox proportional hazards regression with elastic net penalty, and finally a variable set that commonly meets all the criteria from the abovementioned statistical methods. Prediction models using Cox proportional hazards deep neural network (DeepSurv) and Cox proportional hazards regression were constructed in the training set (80% of the total sample) using input feature sets selected from the abovementioned strategies and progressively adding input features by data categories to examine the relative contribution of each data type to the predictive performance for CVD risk. Performance evaluations of the DeepSurv and Cox proportional hazards regression models for CVD risk were conducted in the test set (20% of the total sample) with Unos concordance statistics (C-index), which is the most up-to-date evaluation metrics for the survival models with right censored data. Results: After the comprehensive review, data synthesis, and availability check, a total of 31 risk factors in the categories of sociodemographic, clinical laboratory test and measurement, lifestyle behavior, family history, underlying medical conditions, dental health, medication, and environmental exposure were identified in the NHIS-NSC linked to environmental exposure data. Among the models constructed with different variable selection methods, using statistically significant variables for DeepSurv (Unos C-index: 0.7069) and all of the variables for Cox proportional hazards regression (Unos C-index: 0.7052) showed improved predictive performance for CVD risk, which was a statistically significant increase (p-value for difference in Unos C-index: <0.0001 for both comparisons) compared to the models with basic clinical factors (age, sex, and body mass index), respectively. When all and statistically significant variables in each data category from sociodemographic to environmental exposure were progressively added as input features into DeepSurv and Cox proportional hazards regression for predictive modeling for CVD risk, the DeepSurv model with statistically significant variables pertaining to the sociodemographic factors, clinical laboratory test and measurement, and lifestyle behavior data showed the notable performance that outperformed Cox proportional hazards regression model with statistically significant variables added up to the medication category. Extensive data linkage to environmental exposure on cumulative particulate matter and urban green space offered only marginal improvement for the predictive performance of DeepSurv and Cox proportional hazards regression models for CVD risk. Conclusion: To obtain the best predictive performance of DeepSurv model for CVD risk with minimum number of input features, information on sociodemographic, clinical laboratory test and measurement, and lifestyle behavior should be primarily collected and used as input features in the NHIS-NSC. Also, the overall performance of DeepSurv for CVD risk assessment was improved with a hybrid approach using statistically significant variables from Cox proportional hazards regression as input features. When all the data categories in the NHIS-NSC linked to environmental exposure data are available, progressively adding variables in each data category could incrementally increase the predictive performance of DeepSurv model for CVD risk with the hybrid approach. Data linkage to the environmental exposure with residential area code in the NHIS-NSC offered marginally improved performance for CVD risk in both DeepSurv model with the hybrid approach and Cox proportional hazards regression model.배경 및 목적: 심혈관질환 위험평가 및 예측모델링에서 다양한 심혈관질환 위험인자들의 모델 성능향상에 대한 기여도는 논란의 요지로 보고되어왔다. 또한, 지속적으로 증가하는 활용 가능한 심혈관질환 관련 데이터의 종류와 양에도 불구하고 포괄적인 심혈관질환 위험평가와 최적의 예측 모형 개발을 위해 데이터를 어느 범위와 수준까지 수집해야 하는지에 대한 근거는 부족한 현황이다. 본 연구에서는 콕스 모형과 결합된 딥러닝 기반 생존분석 접근법 및 콕스 모형을 활용한 심혈관질환 위험평가와 예측모델링에서 헬스케어-환경 연계 데이터 활용방법 및 범주에 따른 모델 성능향상에 대한 기여도를 평가하고자 하였다. 연구 방법: 전통적 심혈관질환 위험 평가 도구 및 관찰 연구들에 포함 된 심혈관질환 위험요인 관련 변수들을 체계적 문헌고찰 방법론을 활용하여 의학연구 문헌데이터베이스 (PubMed and Embase)에서 포괄적으로 정보를 수집하였다. 미세먼지 누적장기노출 및 도시녹지면적에 대한 환경 노출 데이터와 연계 된 국민건강보험공단 표본코호트, (National Health Insurance Service-National Sample Cohort, NHIS-NSC)에서 각 심혈관질환 위험인자들의 데이터 확보 가능성을 검토하였다. NHIS-NSC를 기준으로 2009년에서 2010년 사이에 국가건강검진을 받은 40세 이상 대상자 중 과거 심혈관질환 병력이 없는 대상자 137,249명의 환자에 대한 정보를 수집하여 2011 년 1 월 1 일부터 2013 년 12 월 31 일까지 신규 발생한 심혈관질환에 대해 시간 경과에 따라 추적 조사하였다. 통계 기반 변수선택 방법은 콕스비례위험모형에서 통계적 유의성, 최소 (최상의) Akaike Information Criteria (AIC)의 하위 집합, elastic net penalty로 정규화 된 콕스비례위험모형에서 선택된 변수 및 위에 언급된 모든 기준을 충족하는 변수 세트로 선정하였다. 위에 명시된 통계적 방법 외 모든 데이터 범주에 속한 변수 및 콕스비례위험모형에서 통계적으로 유의미한 변수 (하이브리드 접근법)를 점진적으로 입력 피쳐로 추가하는 전략으로 딥러닝 기반 생존분석 (Cox proportional hazards deep neural network, DeepSurv) 및 콕스비례위험모형에서 예측 모델들을 훈련 세트 (전체 샘플의 80 %)를 기반으로 개발하였다. DeepSurv 및 콕스비례 위험모형을 활용한 심혈관질환 예측 모델의 성능평가는 생존분석을 활용한 예측 모델링에 가장 적합한 평가지표로 알려진 Unos concordance statistics (C-index)를 사용하여 테스트 세트 (총 샘플의 20 %)에서 수행하였다. 결과: 체계적 문헌고찰, 데이터 취합 및 추출 가능성 검토 후, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관, 가족력, 건강상태, 구강건강, 약물 및 환경 노출 데이터 범주에서 총 31 개의 심혈관질환 위험인자가 지역환경 자료와 연계된 NHIS-NSC에서 확인되었다. 통계 기반 변수선택 방법으로 개발한 심혈관질환 예측 모델 중 콕스비례위험모형에서 통계적으로 유의미한 변수를 DeepSurv에 적용한 하이브리드 접근법이 Uno 's C-index 값 0.7069, 모든 변수를 콕스비례위험모형에 적용한 콕스비례위험모형이 Uno 's C-index 값 0.7052로 나타나 기본 임상 요인 (연령, 성별 및 체질량지수)이 포함된 예측 모델과 비교하여 통계적으로 유의미한 모델 예측력 증가를 보였다 (두 모델 모두 Unos C-index 차이에 대한 p-value : <0.0001). 인구사회학적 특성에서 환경 노출에 이르기까지 각 데이터 범주에서 모두 통계적으로 유의미한 변수들이 심혈관질환 예측 모델링을위한 DeepSurv 및 Cox 비례 위험 회귀에 입력 피쳐로 점진적으로 추가 된 경우, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관 요인 중 통계적으로 유의미한 변수들로 구성된 DeepSurv 모델이 의약품 사용까지 고려한 Cox 비례 위험 회귀를 기반으로 한 모델 보다 뛰어난 성능을 나타냈다. 미세먼지 및 도시녹지면적에 대한 환경 노출 데이터를 거주지를 기반으로 NHIS-NSC와 연계 후 점진적으로 입력 피쳐로 추가 시 DeepSurv 및 콕스비례위험모형을 활용한 심혈관질환 예측 모델링 성능을 통계적으로 유의미한 수준으로 개선하지 못했다. 결론: 최소 입력 피쳐를 갖춘 생존 분석 기반 심혈관질환 예측 모델에서 최상의 성능을 얻으려면 인구사회학적, 건강검진 및 측정 결과, 및 생활습관에 대한 정보를 NHIS-NSC에서 수집하여 DeepSurv의 입력 피쳐로 활용해야한다. 지역환경 자료와 연계된 NHIS-NSC에서 모든 데이터 범주를 사용할 수 있을 때 점진적으로 각 데이터 범주 중 콕스비례위험모형에서 통계적으로 유의미한 심혈관질환 위험인자를 점진적으로 입력 피쳐로 DeepSurv 모델에 추가하는 하이브리드 접근법에서 심혈관질환 예측 모델링 성능이 점차 향상 될 것으로 기대할 수 있다. 주거 지역 코드를 사용한 NHIS-NSC와 환경 노출 데이터 연계는 DeepSurv 및 콕스비례위험모형 모두에서 심혈관질환 예측 모델링 성능이 향상되었지만 통계적으로 유의미한 증가 수준은 아닌 것으로 나타나 환경 노출 데이터 연계 및 적용 시 검토가 필요할 것으로 추정된다.I. Introduction 1 1. Background 1 2. Research problem 4 3. Hypothesis and objective 6 3.1. Hypothesis 6 3.2. Objective 6 II. Materials and methods 8 1. Comprehensive review and identification of cardiovascular disease (CVD) risk factors 8 1.1. Systematic review on variables included in conventional CVD risk assessment tools 8 1.2. Systematic review on traditional and emerging CVD risk factors from observational studies 9 1.3. Integration of the comprehensive list of CVD risk factors 11 1.4. Screening for data availability 11 2. Cohort analysis for measuring strength of association between risk factors and incident cardiovascular disease 11 2.1 Study population and linkage to environmental exposure data 11 2.2. Variable selection and data processing 15 2.3. Population-based cohort analysis 17 3. Predictive modeling using survival analysis: DeepSurv and Cox proportional hazards regression 17 3.1. Model development 17 3.2. Evaluation of the predictive performance of the models 20 III. Results 21 1. Identification and categorization of cardiovascular disease risk factors 21 2. Magnitude of association between selected risk factors with cardiovascular disease 43 3. Model performance evaluation 56 VI. Discussion 68 1. Key findings and contributions 68 2. Comparison to other studies 69 3. Strengths and limitations 73 4. Implications 74 5. Future perspectives 75 V. Conclusion 77 Reference 78 국문초록 88Docto

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

Objectives In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.Methods We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review ", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. The review was registered on PROSPERO.ResultsFrom a total of 648 studies initially retrieved, 68 articles met the inclusion criteria.Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.Conclusions Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice

Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used

Towards PACE-CAD Systems

Despite phenomenal advancements in the availability of medical image datasets and the development of modern classification algorithms, Computer-Aided Diagnosis (CAD) has had limited practical exposure in the real-world clinical workflow. This is primarily because of the inherently demanding and sensitive nature of medical diagnosis that can have far-reaching and serious repercussions in case of misdiagnosis. In this work, a paradigm called PACE (Pragmatic, Accurate, Confident, & Explainable) is presented as a set of some of must-have features for any CAD. Diagnosis of glaucoma using Retinal Fundus Images (RFIs) is taken as the primary use case for development of various methods that may enrich an ordinary CAD system with PACE. However, depending on specific requirements for different methods, other application areas in ophthalmology and dermatology have also been explored. Pragmatic CAD systems refer to a solution that can perform reliably in day-to-day clinical setup. In this research two, of possibly many, aspects of a pragmatic CAD are addressed. Firstly, observing that the existing medical image datasets are small and not representative of images taken in the real-world, a large RFI dataset for glaucoma detection is curated and published. Secondly, realising that a salient attribute of a reliable and pragmatic CAD is its ability to perform in a range of clinically relevant scenarios, classification of 622 unique cutaneous diseases in one of the largest publicly available datasets of skin lesions is successfully performed. Accuracy is one of the most essential metrics of any CAD system's performance. Domain knowledge relevant to three types of diseases, namely glaucoma, Diabetic Retinopathy (DR), and skin lesions, is industriously utilised in an attempt to improve the accuracy. For glaucoma, a two-stage framework for automatic Optic Disc (OD) localisation and glaucoma detection is developed, which marked new state-of-the-art for glaucoma detection and OD localisation. To identify DR, a model is proposed that combines coarse-grained classifiers with fine-grained classifiers and grades the disease in four stages with respect to severity. Lastly, different methods of modelling and incorporating metadata are also examined and their effect on a model's classification performance is studied. Confidence in diagnosing a disease is equally important as the diagnosis itself. One of the biggest reasons hampering the successful deployment of CAD in the real-world is that medical diagnosis cannot be readily decided based on an algorithm's output. Therefore, a hybrid CNN architecture is proposed with the convolutional feature extractor trained using point estimates and a dense classifier trained using Bayesian estimates. Evaluation on 13 publicly available datasets shows the superiority of this method in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. Explainability of AI-driven algorithms has become a legal requirement after Europe’s General Data Protection Regulations came into effect. This research presents a framework for easy-to-understand textual explanations of skin lesion diagnosis. The framework is called ExAID (Explainable AI for Dermatology) and relies upon two fundamental modules. The first module uses any deep skin lesion classifier and performs detailed analysis on its latent space to map human-understandable disease-related concepts to the latent representation learnt by the deep model. The second module proposes Concept Localisation Maps, which extend Concept Activation Vectors by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. This thesis probes many viable solutions to equip a CAD system with PACE. However, it is noted that some of these methods require specific attributes in datasets and, therefore, not all methods may be applied on a single dataset. Regardless, this work anticipates that consolidating PACE into a CAD system can not only increase the confidence of medical practitioners in such tools but also serve as a stepping stone for the further development of AI-driven technologies in healthcare

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scienc