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Machine Learning Approaches to Tumor Estimation of Whole Slide Images

Description

Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing…

Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing (since many assays require a minimum tumor content to report variants at the limit of detection) may all be improved with more accurate and reproducible estimates of tumor content. Currently, tumor percentages of samples submitted for molecular testing are estimated by visual examination of Hematoxylin and Eosin (H&E) stained tissue slides under the microscope by pathologists. These estimations can be automated, expedited, and rendered more accurate by applying machine learning methods on digital whole slide images (WSI).

ContributorsCirelli, Claire (Author) / Yang, Yezhou (Thesis director) / Yalim, Jason (Committee member) / Velu, Priya (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)

Created2022-05

Effects of Training Dataset Variance on Artificial Intelligence Image Generation

Description

This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as…

This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as size, labeling, and format of the training data. The findings suggest that reducing the training dataset size can lead to a decrease in image coherence, indicating that AI models get worse as the training dataset gets smaller. Moreover, the study makes surprising discoveries regarding AI image generation models that are trained on highly varied datasets. In addition, the study involves a survey in which people were asked to rate the subjective realism of the generated images on a scale ranging from 1 to 5 as well as sorting the images into their respective classes. The findings of this study emphasize the importance of considering dataset variance and size as a critical aspect of improving image generation models as well as the implications of using AI technology in the future.

ContributorsPunyamurthula, Rushil (Author) / Carter, Lynn (Thesis director) / Sarmento, Rick (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / Computer Science and Engineering Program (Contributor)

Created2023-05

Comparison of Machine Learning Algorithms for Predicting Breast Cancer Malignancy

Description

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…

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.

ContributorsMallya, Aatmik (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)

Created2023-05

DeepDEMs: Generating Digital Elevation Models (DEMs) of the Moon and Mars from 2-D Images using Deep Learning

Description

This thesis investigates the quality and usefulness of "DeepDEMs" from Moon and Mars images, which are Digital Elevation Models (DEMs) created using deep learning from single optical images. High-resolution DEMs of Moon and Mars are increasingly critical for gaining insights into the slope and the elevation of the terrain in…

This thesis investigates the quality and usefulness of "DeepDEMs" from Moon and Mars images, which are Digital Elevation Models (DEMs) created using deep learning from single optical images. High-resolution DEMs of Moon and Mars are increasingly critical for gaining insights into the slope and the elevation of the terrain in the region which helps in identifying the landing sites of possible manned missions and rovers. However, many locations of interest to scientists who use remote sensing to study the Earth or other planetary bodies have only visible image data coverage, and not repeated stereo image coverage or other data collected specifically for DEM generation. Thus, Earth and planetary scientists, geographers, and other academics want DEMs in many locations where no data resources (repeat coverage or intensive remote sensing campaigns) have been assigned for geomorphic or topographic study. One specific use for deep learning-generated terrain models would be to assess probable sites in the lunar south polar area for NASA's future Artemis III mission which aims to return people to the lunar surface. While conventional techniques (for example, needing two stereo pictures from satellites for photogrammetry) work well, this high-resolution data only covers a small portion of the planets. Furthermore, older approaches need lengthy processing durations as well as human calibration and tweaking to achieve high-quality DEMs. To address the coverage and processing time concerns, we evaluated deep learning algorithms for creating DEMs of the Moon and Mars' surfaces. We explore how the findings of this study may be used to create elevation models for planetary mapping in the future using automated methods.

ContributorsJain, Rini (Author) / Rastogi, Anant (Co-author) / Kerner, Hannah (Thesis director) / Adler, Jacob (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor)

Created2024-05

Scoliosis Recognition with Applied Machine Learning

Description

As the use of Artificial Intelligence (AI) continues to expand and improve across numerous industries, the success with which it has been integrated into the medical sector stands out. Physicians and researchers now utilize AI in many situations. In particular, advancements within the field of detection AI have had a…

As the use of Artificial Intelligence (AI) continues to expand and improve across numerous industries, the success with which it has been integrated into the medical sector stands out. Physicians and researchers now utilize AI in many situations. In particular, advancements within the field of detection AI have had a significant impact on the diagnosis and treatment of scoliosis. Detection AI has been developed to recognize important features within an image, such as malignant tumors in adults. For a scoliosis patient, a detection model can manipulate radiograph images to create masks and highlight important features that could be missed by the human eye, such as minute changes in a cell, and create binary masks of a spine. Using a popular convolution neural network (CNN) to examine datasets of scoliosis x-rays, provided by Hanger Inc [6], this paper examines the capabilities of machine learning to effectively differentiate the spine from other bones in x-ray imagery and to identify scoliosis in affected patients. Based on the results of the project, several issues were discovered that, if resolved, could improve the overall accuracy of the model, which would allow it to potentially find its own place within medical workflows to expedite the scoliosis design process.

ContributorsCooney, Sloan (Author) / Kerner, Hannah (Thesis director) / Clark, Geoffrey (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)

Created2024-05

Convolutional Neural Networks for Facial Expression Recognition

Description

This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can…

This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.

ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)

Created2016-05

Learning Policies for Model-Based Reinforcement Learning Using Distributed Reward Formulation

Description

This work explores combining state-of-the-art \gls{mbrl} algorithms focused on learning complex policies with large state-spaces and augmenting them with distributional reward perspective on \gls{rl} algorithms. Distributional \gls{rl} provides a probabilistic reward formulation as opposed to the classic \gls{rl} formulation which models the estimation of this distributional return. These probabilistic reward…

This work explores combining state-of-the-art \gls{mbrl} algorithms focused on learning complex policies with large state-spaces and augmenting them with distributional reward perspective on \gls{rl} algorithms. Distributional \gls{rl} provides a probabilistic reward formulation as opposed to the classic \gls{rl} formulation which models the estimation of this distributional return. These probabilistic reward formulations help the agent choose highly risk-averse actions, which in turn makes the learning more stable. To evaluate this idea, I experiment in simulation on complex high-dimensional environments when subject under different noisy conditions.

ContributorsAgarwal, Nikhil (Author) / Ben Amor, Heni (Thesis advisor) / Phielipp, Mariano (Committee member) / DV, Hemanth (Committee member) / Arizona State University (Publisher)

Created2021

Machine learning on Mars: a new lens on data from planetary exploration missions

Description

There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance…

There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.

ContributorsKerner, Hannah Rae (Author) / Bell, James F. (Thesis advisor) / Ben Amor, Heni (Thesis advisor) / Wagstaff, Kiri L (Committee member) / Hardgrove, Craig J (Committee member) / Shirzaei, Manoochehr (Committee member) / Arizona State University (Publisher)

Created2019

Prediction of Binding Affinity of T cell Receptor and Antigens using Deep Neural Networks

Description

Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict…

Immunotherapy is an effective treatment for cancer which enables the patient's immune system to recognize tumor cells as pathogens. In order to design an individualized treatment, the t cell receptors (TCR) which bind to a tumor's unique antigens need to be determined. We created a convolutional neural network to predict the binding affinity between a given TCR and antigen to enable this.

ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)

Created2020-12

Facial Expression Recognition Using Machine Learning

Description

In recent years, the development of new Machine Learning models has allowed for new technological advancements to be introduced for practical use across the world. Multiple studies and experiments have been conducted to create new variations of Machine Learning models with different algorithms to determine if potential systems would prove…

In recent years, the development of new Machine Learning models has allowed for new technological advancements to be introduced for practical use across the world. Multiple studies and experiments have been conducted to create new variations of Machine Learning models with different algorithms to determine if potential systems would prove to be successful. Even today, there are still many research initiatives that are continuing to develop new models in the hopes to discover potential solutions for problems such as autonomous driving or determining the emotional value from a single sentence. One of the current popular research topics for Machine Learning is the development of Facial Expression Recognition systems. These Machine Learning models focus on classifying images of human faces that are expressing different emotions through facial expressions. In order to develop effective models to perform Facial Expression Recognition, researchers have gone on to utilize Deep Learning models, which are a more advanced implementation of Machine Learning models, known as Neural Networks. More specifically, the use of Convolutional Neural Networks has proven to be the most effective models for achieving highly accurate results at classifying images of various facial expressions. Convolutional Neural Networks are Deep Learning models that are capable of processing visual data, such as images and videos, and can be used to identify various facial expressions. The purpose of this project, I focused on learning about the important concepts of Machine Learning, Deep Learning, and Convolutional Neural Networks to implement a Convolutional Neural Network that was previously developed by a recommended research paper.

ContributorsFrace, Douglas R (Author) / Demakethepalli Venkateswara, Hemanth Kumar (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)

Created2020-05

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