Browsing by Subject "Medical Imaging"
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Item Machine Learning Methods with Emphasis on Cancerous Tissue Recognition(2018-08) Stanitsas, PanagiotisToday, vast and unwieldy data collections are regularly being generated and analyzed in hopes of supporting an ever-expanding range of challenging sensing applications. Modern inference schemes usually involve millions of parameters to learn complex real-world tasks, which creates the need for large annotated datasets for training. For several visual learning applications, collecting large amounts of annotated data is either challenging or very expensive; one such domain is medical image analysis. In this thesis, machine learning methods were devised with emphasis on Cancerous Tissue Recognition (CTR) applications. First, a lightweight active constrained clustering scheme was developed for the processing of image data which capitalizes on actively acquired pairwise constraints. The proposed methodology introduces the use of the Silhouette values, conventionally used for measuring clustering performance, in order to rank the degree of information content of the various samples. Second, an active selection framework that operates in tandem with Convolutional Neural Networks (CNNs) was constructed for CTR. In the presence of limited annotations, alternative (or sometimes complementary) venues were explored in an effort to restrain the high expenditure of collecting image annotations required by CNN-based schemes. Third, a Symmetric Positive Definite (SPD) image representation was derived for CTR, termed Covariance Kernel Descriptor (CKD) which consistently outperformed a large collection of popular image descriptors. Even though the CKD successfully describes the tissue architecture for small image regions, its performance decays when implemented on larger slide regions or whole tissue slides due to the larger variability that tissue exhibits at that level, since different types of tissue can be present as the regions grow (healthy, benign disease, malignant disease). Fourth, to leverage the recognition capability of the CKDs to larger slide regions, the Weakly Annotated Image Descriptor (WAID) was devised as the parameters of classifier decision boundaries in a multiple instance learning framework. Fifth, an Information Divergence and Dictionary Learning (IDDL) scheme for SPD matrices was developed for identifying appropriate geometries and similarities for SPD matrices and was successfully tested on a diverse set of recognition problems including activity, object, and texture recognition as well as CTR. Finally, a transition of IDDL to an unsupervised setup was developed, dubbed alpha-beta-KMeans, to address the problem of learning information divergences while clustering SPD matrices in the absence of labeled data.Item Medical screening: medical imag[in]ing, the body, and the self.(2011-06) Cohen, Sara JoThis dissertation examines representations of medical imaging technologies in order to understand how sick artists (writers, painters, and filmmakers) use diagnostic technologies in their work to assert their subjectivity in the face of medicine's efforts to objectify them. It uses the phrase "medical imag[in]ing technology" to refer broadly to visual and aural aids that have assisted physicians and surgeons with their work, and focuses primarily on the X-ray, the microscope, and the telephone, which had a brief stint as a proto-ultrasound device at President James A. Garfield's deathbed. Its chapters engage a series of texts connected in their efforts to understand the body through medical imag[in]ing technologies: Thomas Mann's 1924 novel The Magic Mountain, Alice James's diary, Frida Kahlo's paintings, Sergio Leone's westerns, Norman Mailer's films and writings, and the narratives surrounding the James-Younger Gang's 1876 raid on Northfield, Minnesota, and the 1881 assassination of President Garfield. I posit that these nineteenth and twentieth century texts and their efforts to understand both illness and medicine through medical imaging technologies anticipate contemporary efforts to involve patients in their health care through online medical records, illness blogs, and illness-based social networking sites.Item Solving Inverse Problems in Medical Imaging Using Statistical Signal Processing and Deep Learning(2020-02) Hosseini, Seyed Amir HosseinSolving inverse problems remain an active research area in various fields to study the cause of a phenomenon by observing the effects. In particular, such efforts are well grounded in medical imaging applications where inverse problems naturally arise due to the imaging target being either inaccessible or invisible to human eyes. Non-regularity or ill-conditioning is a major challenge in such situations which is a direct consequence of limited observations/measurements being available. Medical imaging applications have classically incorporated domain-specific knowledge about the forward encoding operator to regularize the pertaining inverse problem. Deep learning has recently received high interest as an alternative approach for data-driven regularization. In principle, such regularization can be achieved by acquiring more measurements. However, sufficient measurements are usually unavailable due to practical considerations. For instance, in electromagnetic source imaging (ESI) using electroencephalography (EEG), number of recording electrodes can not be excessively increased due to subsequent distortions that electrodes contacts can cause in the medium. This limitation adversely affects the spatial resolution of EEG-based ESI. Another example is magnetic resonance imaging (MRI) where increasing imaging resolution trades off with noise properties, temporal resolution and scan duration. High-resolution MRI can become prohibitively long especially when a sequence of images need to be obtained. Thus, accelerated MRI techniques are required to reconstruct high-resolution images using less data. The present thesis contributes to solving regularization issues pertaining to inverse problems in medical imaging with a focus on high-resolution electromagnetic source imaging (ESI) using electroencephalography (EEG) and accelerated high resolution magnetic resonance imaging (MRI) with high fidelity. The ESI spatial resolution and the brain full coverage is shown to significantly improve when simultaneous scalp EEG and intracranial EEG recordings are jointly utilized. Additionally, a new ESI technique is proposed that incorporates forward modeling uncertainties into inverse problem formulations, considerably improving localization accuracy and recovering underlying source dynamics and extents when such uncertainties are properly determined and modeled. A novel deep learning-based reconstruction technique is proposed for accelerated MRI using a recurrent neural network architecture that offers desired noise properties compared to conventional techniques, particularly at higher acceleration rates. Furthermore, a new deep learning methodology is developed that unrolls inverse problem optimization algorithms into dense recurrent neural networks for improved reconstruction performance without requiring extra computational power or increasing reconstruction time.