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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
The Future Of Artificial Intelligence In The Healthcare Industry, Erika Bonnist
The Future Of Artificial Intelligence In The Healthcare Industry, Erika Bonnist
Honors Theses
Technology has played an immense role in the evolution of healthcare delivery for the United States and on an international scale. Today, perhaps no innovation offers more potential than artificial intelligence. Utilizing machine intelligence as opposed to human intelligence for the purposes of planning, offering solutions, and providing insights, AI has the ability to alter traditional dynamics between doctors, patients, and administrators; this reality is now producing both elation at artificial intelligence's medical promise and uncertainty regarding its capacity in current systems. Nevertheless, current trends reveal that interest in AI among healthcare stakeholders is continuously increasing, and with the current …
Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti
Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti
Honors Theses
Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical …
Variable Autoencoders For Biosensor Data Augmentation, Solomon Kim
Variable Autoencoders For Biosensor Data Augmentation, Solomon Kim
Honors Theses
Over the past decade machine learning and artificial intelligence's resurgence spawned the desire to mimic human creative ability. Initially attempts to create images, music, and text flooded the community, though little has been learned regarding constrained, one-dimensional data generation. This paper demonstrates a variational autoencoder approach to this problem. By modeling biosensor current and concentration data we aim to augment the existing dataset. In training a multi-layer neural network based encoder and decoder we were able to generate realistic, original samples., These results demonstrate the ability to realistically augment datasets, improving training of machine learning models designed to predict concentration …
Estimating Heading From Optic Flow With Neural Networks, Natalie T. Maus
Estimating Heading From Optic Flow With Neural Networks, Natalie T. Maus
Honors Theses
Humans have a remarkable ability to estimate their direction of self-motion, or heading, based on visual input stimulus (optic flow). Machines, on the other hand, have a difficult time with this task, especially when flow is introduced that is inconsistent with the motion of the observer. For example, when moving objects enter the field of view, their motion provides inconsistent flow data which often disrupts heading estimates of current heading estimation models. We investigate the ability of neural networks to estimate heading from optic flow data and the limitations of these models when different variations of inconsistent flow are introduced. …
Convolutional Audio Source Separation Applied To Drum Signal Separation, Marius Orehovschi
Convolutional Audio Source Separation Applied To Drum Signal Separation, Marius Orehovschi
Honors Theses
This study examined the task of drum signal separation from full music mixes via both classical methods (Independent Component Analysis) and a combination of Time-Frequency Binary Masking and Convolutional Neural Networks. The results indicate that classical methods relying on predefined computations do not achieve any meaningful results, while convolutional neural networks can achieve imperfect but musically useful results. Furthermore, neural network performance can be improved by data augmentation via transposition – a technique that can only be applied in the context of drum signal separation.
Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya
Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya
Honors Theses
Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …