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- All Subjects: deep learning
- Creators: School of Mathematical and Statistical Sciences
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
This thesis is part of a collaboration between ASU’s Interactive Robotics Laboratory and NASA’s Jet Propulsion Laboratory. In this thesis, the training pipeline from Sharma’s paper “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks” was modified to perform pose estimation on a complex object - specifically, a segment of a hollow truss. After initial attempts to replicate the architecture used in the paper and train solely on synthetic images, a combination of synthetic dataset generation and transfer learning on an ImageNet-pretrained AlexNet model was implemented to mitigate the difficulty of gathering large amounts of real-world data. Experimentation with pose estimation accuracy and hyperparameters of the model resulted in gradual test accuracy improvement, and future work is suggested to improve pose estimation for complex objects with some form of rotational symmetry.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
particularly emergent class of cardiovascular diseases and account for significant cardiovascular morbidity and mortality worldwide. Computational simulations of aortic flows are growing increasingly important as tools for gaining understanding of these pathologies and for planning their surgical repair. In vitro experiments are required to validate these simulations against real world data, and a pulsatile flow pump system can provide physiologic flow conditions characteristic of the aorta.
This dissertation presents improved experimental techniques for in vitro aortic blood flow and the increasingly larger parts of the human cardiovascular system. Specifically, this work develops new flow management and measurement techniques for cardiovascular flow experiments with the aim to improve clinical evaluation and treatment planning of aortic diseases.
The hypothesis of this research is that transient flow driven by a step change in volume flux in a piston-based pulsatile flow pump system behaves differently from transient flow driven by a step change in pressure gradient, the development time being substantially reduced in the former. Due to this difference in behavior, the response to a piston-driven pump can be predicted in order to establish inlet velocity and flow waveforms at a downstream phantom model.
The main objectives of this dissertation were: 1) to design, construct, and validate a piston-based flow pump system for aortic flow experiments, 2) to characterize temporal and spatial development of start-up flows driven by a piston pump that produces a step change from zero flow to a constant volume flux in realistic (finite) tube geometries for physiologic Reynolds numbers, and 3) to develop a method to predict downstream velocity and flow waveforms at the inlet of an aortic phantom model and determine the input waveform needed to achieve the intended waveform at the test section. Application of these newly improved flow management tools and measurement techniques were then demonstrated through in vitro experiments in patient-specific coarctation of aorta flow phantom models manufactured in-house and compared to computational simulations to inform and execute future experiments and simulations.