Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

abstract: Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring. The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain. The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models. The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Computer-aided diagnosis system for bone fracture detection using machine learning algorithms

Diagnostic imaging technology has revolutionized the healthcare industry by allowing more accurate and earlier diagnosis of diseases. This technology reduces the need for invasive procedures such as surgery and enhances the quality of patient care. Several machine learning algorithms like SVM, K-mean clustering and UNET have been demonstrated to be capable of solving classification, detection, and segmentation problems in medical imaging, as well as being used for super-resolution techniques. The purpose of this thesis is to examine machine learning and image processing methods for four key challenges in medical image analysis. The first one is the segmentation of medical images. The second challenge involves implementing super-resolution techniques for medical images. Third, using image processing methods in order to diagnose the abnormalities. The fourth contribution is to enrich the image information by mapping of medical images between different modalities using deep neural models. In this research, all contributions aim at developing an end-to-end model that can detect fractures automatically or be used as a clinical assistant to reduce errors. As the first contribution, the thesis presents a multi-stage novel approach for bone segmentation in X-ray images using faster region-based convolutional neural network (R-CNN) and distance regularized level set evolution (DRLSE) algorithms. A hybrid model utilizing deep neural network (DNN) and image processing techniques are proposed to segment the bones in two stages. Our model is more robust to the changes in X-ray images, as well as applicable to bones that are misplaced. Additionally, we have used transfer learning to reduce the amount of time and effort required to collect and label the data. As the second contribution, DNN models are used to enhance the resolution of medical images. CNN and generative adversarial network have been used as super-resolution techniques to achieve high-resolution medical images. The analysis includes subjective and objective evaluations of different models on regions with or without fractures to compare them with our model. The third contribution involves applying different image analysis methods to X-ray images in order to detect fractures with the minimum amount of human intervention. By using entropy and intensity, we have also attempted to identify regions of interest that have a higher probability of having fractures. We also evaluate the effect of super-resolution technique on the saliency map with and without fractures. Lastly, we present image-to-image mapping by using variational autoencoders and generative adversarial networks to reduce the cost of diagnosis and medical images retrievals. We have attempted to map X-ray images to MRIs in this section in order to fuse the high diagnostic information existing in MRIs, for enhancing the matched X-ray 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

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf