McNair Scholars Research Journal Volume V

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Machine Learning in Medical Image Analysis

Machine learning is playing a pivotal role in medical image analysis. Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems. Particularly, with the wide application of deep learning approaches, the performance of medical image analysis has been significantly improved. In this thesis, we investigate machine learning methods for two key challenges in medical image analysis: The first one is segmentation of medical images. The second one is learning with weak supervision in the context of medical imaging. The first main contribution of the thesis is a series of novel approaches for image segmentation. First, we propose a framework based on multi-scale image patches and random forests to segment small vessel disease (SVD) lesions on computed tomography (CT) images. This framework is validated in terms of spatial similarity, estimated lesion volumes, visual score ratings and was compared with human experts. The results showed that the proposed framework performs as well as human experts. Second, we propose a generic convolutional neural network (CNN) architecture called the DRINet for medical image segmentation. The DRINet approach is robust in three different types of segmentation tasks, which are multi-class cerebrospinal fluid (CSF) segmentation on brain CT images, multi-organ segmentation on abdomen CT images, and multi-class tumour segmentation on brain magnetic resonance (MR) images. Finally, we propose a CNN-based framework to segment acute ischemic lesions on diffusion weighted (DW)-MR images, where the lesions are highly variable in terms of position, shape, and size. Promising results were achieved on a large clinical dataset. The second main contribution of the thesis is two novel strategies for learning with weak supervision. First, we propose a novel strategy called context restoration to make use of the images without annotations. The context restoration strategy is a proxy learning process based on the CNN, which extracts semantic features from images without using annotations. It was validated on classification, localization, and segmentation problems and was superior to existing strategies. Second, we propose a patch-based framework using multi-instance learning to distinguish normal and abnormal SVD on CT images, where there are only coarse-grained labels available. Our framework was observed to work better than classic methods and clinical practice.Open Acces

Signal transformation and noise correlation in the primate dorsal stream during sensory decision-making

Neuroscientists have long sought a link between the activity of single neurons and our thoughts, perceptions and ultimately our mental experiences. As our senses provide the input into the brain, understanding the computations that transform signals along the sensory pathways has remained central to this endeavor. Remarkable progress has been made by studying neural correlates of perceptual decisions in motion-processing and oculomotor areas of the primate brain. In particular, when monkeys indicate their decisions about the direction of motion with eye movements, neurons in the middle temporal area (MT) represent the instantaneous motion evidence and neurons in the lateral intraparietal area (LIP) resemble the integration of motion evidence, effectively transforming the sensory signal into a decision variable. In the main body of this thesis, I describe the results of an effort to measure the sensorimotor transformation between MT and LIP on single trials. First, I describe a motion-discrimination task that is amenable to reverse correlation analysis, allowing the experimenter to measure the temporal dependencies of neural responses and choices on the instantaneous motion energy. I then use a unified statistical framework to analyze simultaneous recordings from both areas during decision-making. Primarily, I found that MT neurons exhibited time-varying sensitivity to motion direction, with important consequences for the behavior and neurophysiology in downstream areas. Individual LIP neurons also carried a signature of an integrated motion signal in their spike rates, however, it was unlikely that this signal results from direct MT input. Finally, I show that a biologically plausible simple decoder can perform as well as the monkey at coarse direction-discrimination task. In the appendix, I describe the results of pharmacological inactivations of MT and LIP and statistical models of single trial dynamics in LIP that were performed in collaboration with fellow graduate students, Leor Katz and Kenneth Latimer, respectively.Neuroscienc

Exploring a chromatic oblique effect.

For centuries, military forces have used camouflage to obscure potential targets from the enemy. Because the eye is fairly adept at picking out edges, colors, and bright areas, camouflage is often used to degrade these qualities from human detection. The purpose of this thesis was to investigate the role of certain spatial, temporal, and chromatic features on the human visual system and how these features may aid the quest for better camouflage. Methods: Test patterns were spatio-temporal raised cosines of varying orientation (horizontal or vertical and oblique), spatial frequency (1, 3, and 7 cpd), and modulated at 2.0 Hz. Color contrast thresholds were determined from 16 different red-green color mixture ratios. This methodology eliminates the problems with luminance artifacts and the need to determine exact equiluminance. Results: The data formed an ellipse with the half-length measuring color ldiscrimination and the half-width measuring brightness discrimination. A maximum likelihood method was used to fit the data. Three of the four subjects showed a 3 cpd chromatic oblique effect, while the 1 and 7 cpd achromatic and chromatic oblique effect was inconsistent across subjects. Conclusions: While real-world objects are more complex than laboratory stimuli, knowledge of spatial and chromatic qualities that inhibit detection will aid the quest for better camouflagehttp://archive.org/details/exploringchromat00currMajor, United States Marine CorpsApproved for public release; distribution is unlimited