Innovations in Medical Image Analysis and Explainable AI for Transparent Clinical Decision Support Systems

This thesis explores innovative methods designed to assist clinicians in their everyday practice, with a particular emphasis on Medical Image Analysis and Explainability issues. The main challenge lies in interpreting the knowledge gained from machine learning algorithms, also called black-boxes, to provide transparent clinical decision support systems for real integration into clinical practice. For this reason, all work aims to exploit Explainable AI techniques to study and interpret the trained models. Given the countless open problems for the development of clinical decision support systems, the project includes the analysis of various data and pathologies. The main works are focused on the most threatening disease afflicting the female population: Breast Cancer. The works aim to diagnose and classify breast cancer through medical images by taking advantage of a first-level examination such as Mammography screening, Ultrasound images, and a more advanced examination such as MRI. Papers on Breast Cancer and Microcalcification Classification demonstrated the potential of shallow learning algorithms in terms of explainability and accuracy when intelligible radiomic features are used. Conversely, the union of deep learning and Explainable AI methods showed impressive results for Breast Cancer Detection. The local explanations provided via saliency maps were critical for model introspection, as well as increasing performance. To increase trust in these systems and aspire to their real use, a multi-level explanation was proposed. Three main stakeholders who need transparent models have been identified: developers, physicians, and patients. For this reason, guided by the enormous impact of COVID-19 in the world population, a fully Explainable machine learning model was proposed for COVID-19 Prognosis prediction exploiting the proposed multi-level explanation. It is assumed that such a system primarily requires two components: 1) inherently explainable inputs such as clinical, laboratory, and radiomic features; 2) Explainable methods capable of explaining globally and locally the trained model. The union of these two requirements allows the developer to detect any model bias, the doctor to verify the model findings with clinical evidence, and justify decisions to patients. These results were also confirmed for the study of coronary artery disease. In particular machine learning algorithms are trained using intelligible clinical and radiomic features extracted from pericoronaric adipose tissue to assess the condition of coronary arteries. Eventually, some important national and international collaborations led to the analysis of data for the development of predictive models for some neurological disorders. In particular, the predictivity of handwriting features for the prediction of depressed patients was explored. Using the training of neural networks constrained by first-order logic, it was possible to provide high-performance and explainable models, going beyond the trade-off between explainability and accuracy

Exploring RCNN for the automated analysis of paramagnetic rim lesions in Multiple Sclerosis MRI

In multiple sclerosis, lesions with a peripheral paramagnetic rim is a negative prognostic imaging biomarker and represents a potential outcome measure in MRI-based clinical trials. Nowadays, the presence or absence of paramagnetic rims is determined through visual inspection by medical experts, which is tedious, time consuming and prone to observer variability. So far, few solutions to the automated classification of rims problem have been proposed. These studies present limitations that represent an obstacle to full automation of the rim analysis process and its large-scale application. Our goal is to implement and assess a fully automated algorithm capable of identifying rim lesions in MRI. In this work, we explore a Region-proposal CNN deep learning approach to solve the fully automated rim lesions classification problem that perform instance segmentation by object detection and have shown promising results in recent challenges, particularly in medical imaging. After different approaches focus on implifying the task, Mask RCNN with MobileNet v2 as backbone using attention gaussian filtering to the input images showed better performance than the rest with rates of 0.42 TPR and 0.61 FPR for the test set. However, the achieved results reveal the weaknesses of our approach and the difficulty of our classification problem

Decision fusion in healthcare and medicine : a narrative review

Objective: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. Background: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. Methods: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. Conclusions: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Low-latency big data visualisation

Diese Arbeit hat sich zum Ziel gesetzt, Methoden aufzuzeigen, „Big-Data“-Archive zu organisieren und zentrale Elemente der enthaltenen Informationen zu visualisieren. Anhand von drei wissenschaftlichen Experimenten werde ich zwei „Big-Data“- Herausforderungen, Datenvolumen (Volume) und Heterogenität (Variety), untersuchen und eine Visualisierung im Browser präsentieren, die trotz reduzierter Datenrate die wesentliche Information in den Datensätzen enthält

Multiple instance learning under real-world conditions

Multiple instance learning (MIL) is a form of weakly-supervised learning that deals with data arranged in sets called bags. In MIL problems, a label is provided for bags, but not for each individual instance in the bag. Like other weakly-supervised frameworks, MIL is useful in situations where obtaining labels is costly. It is also useful in applications where instance labels cannot be observed individually. MIL algorithms learn from bags, however, prediction can be performed at instance- and bag-level. MIL has been used in several applications from drug activity prediction to object localization in image. Real-world data poses many challenges to MIL methods. These challenges arise from different problem characteristics that are sometimes not well understood or even completely ignored. This causes MIL methods to perform unevenly and often fail in real-world applications. In this thesis, we propose methods for both classification levels under different working assumptions. These methods are designed to address challenging problem characteristics that arise in real-world applications. As a first contribution, we survey these characteristics that make MIL uniquely challenging. Four categories of characteristics are identified: the prediction level, the composition of bags, the data distribution types and the label ambiguity. Each category is analyzed and related state-of-the-art MIL methods are surveyed. MIL applications are examined in light of these characteristics and extensive experiments are conducted to show how these characteristics affect the performance of MIL methods. From these analyses and experiments, several conclusions are drawn and future research avenues are identified. Then, as a second contribution, we propose a method for bag classification which relies on the identification of positive instances to train an ensemble of instance classifiers. The bag classifier uses the predictions made on instances to infer bag labels. The method identifies positive instances by projecting the instances into random subspaces. Clustering is performed on the data in these subspaces and positive instances are probabilistically identified based on the bag label of instances in clusters. Experiments show that the method achieves state-of-theart performance while being robust to several characteristics identified in the survey. In some applications, the instances cannot be assigned to a positive or negative class. Bag classes are defined by a composition of different types of instances. In such cases, interrelations between instances convey the information used to discriminate between positive and negative bags. As a third contribution, we propose a bag classification method that learns under these conditions. The method is a applied to predict speaker personality from speech signals represented as bags of instances. A sparse dictionary learning algorithm is used to learn a dictionary and encode instances. Encoded instances are embedded in a single feature vector summarizing the speech signal. Experimental results on real-world data reveal that the proposed method yields state-of-the-art accuracy results while requiring less complexity than commonly used methods in the field. Finally, we propose two methods for querying bags in a multiple instance active learning (MIAL) framework. In this framework the objective is to train a reliable instance classifier using a minimal amount of labeled data. Single instance methods are suboptimal is this framework because they do not account the bag structure of MIL. The proposed methods address the problem from different angles. One aims at directly refining the decision boundary, while the other leverage instance and bag labels to query instances in the most promising clusters. Experiments are conducted in an inductive and transductive setting. Results on data from 3 application domains show that leveraging bag structure in this MIAL framework is important to effectively reduce the number of queries necessary to attain a high level of classification accuracy. This thesis shows that real-world MIL problems pose a wide range of challenges. After an in-depth analysis, we show experimentally that these challenges have a profound impact on the performance of MIL algorithms. We propose methods to address some of these challenges and validate them on real-world data sets. We also identify future directions for research and remaining open problems