breast cancer thesis phd

Ph.D. thesis : Predicting the Breast Cancer response to Chemotherapy by Image Processing and Deep Learning

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Electronic Theses and Dissertations

Computer aided diagnosis system for breast cancer using deep learning..

Asma Baccouche , University of Louisville Follow

Date on Master's Thesis/Doctoral Dissertation

Document type.

Doctoral Dissertation

Degree Name

Computer Engineering and Computer Science

Degree Program

Computer Science and Engineering, PhD

Committee Chair

Elmaghraby, Adel

Committee Co-Chair (if applicable)

Garcia-Zapirain, Maria Begona

Committee Member

Sierra-Sosa, Daniel

Gentili, Monica

Park, Juw Won

Author's Keywords

Medical imaging; breast cancer; deep learning; CAD; artificial intelligence; computer vision

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application.

Recommended Citation

Baccouche, Asma, "Computer aided diagnosis system for breast cancer using deep learning." (2022). Electronic Theses and Dissertations. Paper 3931. https://doi.org/10.18297/etd/3931

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Theses & Dissertations: Cancer Research

Theses/dissertations from 2024 2024.

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FGFR4 glycosylation and processing in cholangiocarcinoma promote cancer signaling , Andrew J. Phillips

Theses/Dissertations from 2020 2020

Cooperativity of CCNE1 and FOXM1 in High-Grade Serous Ovarian Cancer , Lucy Elge

Characterizing the critical role of metabolic and redox homeostasis in colorectal cancer , Danielle Frodyma

Genomic and Transcriptomic Alterations in Metabolic Regulators and Implications for Anti-tumoral Immune Response , Ryan J. King

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The Cellular Origin and Molecular Drivers of Claudin-Low Mammary Cancer , Patrick D. Raedler

Mitochondrial Metabolism as a Therapeutic Target for Pancreatic Cancer , Simon Shin

Development of Fluorescent Hyaluronic Acid Nanoparticles for Intraoperative Tumor Detection , Nicholas E. Wojtynek

Theses/Dissertations from 2019 2019

The role of E3 ubiquitin ligase FBXO9 in normal and malignant hematopoiesis , R. Willow Hynes-Smith

BRCA1 & CTDP1 BRCT Domainomics in the DNA Damage Response , Kimiko L. Krieger

Targeted Inhibition of Histone Deacetyltransferases for Pancreatic Cancer Therapy , Richard Laschanzky

Human Leukocyte Antigen (HLA) Class I Molecule Components and Amyloid Precursor-Like Protein 2 (APLP2): Roles in Pancreatic Cancer Cell Migration , Bailee Sliker

Theses/Dissertations from 2018 2018

FOXM1 Expression and Contribution to Genomic Instability and Chemoresistance in High-Grade Serous Ovarian Cancer , Carter J. Barger

Overcoming TCF4-Driven BCR Signaling in Diffuse Large B-Cell Lymphoma , Keenan Hartert

Functional Role of Protein Kinase C Alpha in Endometrial Carcinogenesis , Alice Hsu

Functional Signature Ontology-Based Identification and Validation of Novel Therapeutic Targets and Natural Products for the Treatment of Cancer , Beth Neilsen

Elucidating the Roles of Lunatic Fringe in Pancreatic Ductal Adenocarcinoma , Prathamesh Patil

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Metabolic Reprogramming of Pancreatic Ductal Adenocarcinoma Cells in Response to Chronic Low pH Stress , Jaime Abrego

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The Role of EHD2 in Triple-Negative Breast Cancer Tumorigenesis and Progression , Timothy A. Bielecki

Perturbing anti-apoptotic proteins to develop novel cancer therapies , Jacob Contreras

Role of Ezrin in Colorectal Cancer Cell Survival Regulation , Premila Leiphrakpam

Evaluation of Aminopyrazole Analogs as Cyclin-Dependent Kinase Inhibitors for Colorectal Cancer Therapy , Caroline Robb

Identifying the Role of Janus Kinase 1 in Mammary Gland Development and Breast Cancer , Barbara Swenson

DNMT3A Haploinsufficiency Provokes Hematologic Malignancy of B-Lymphoid, T-Lymphoid, and Myeloid Lineage in Mice , Garland Michael Upchurch

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EHD1 As a Positive Regulator of Macrophage Colony-Stimulating Factor-1 Receptor , Luke R. Cypher

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Role of CBL-family Ubiquitin Ligases as Critical Negative Regulators of T Cell Activation and Functions , Benjamin Goetz

Exploration into the Functional Impact of MUC1 on the Formation and Regulation of Transcriptional Complexes Containing AP-1 and p53 , Ryan L. Hanson

DNA Polymerase Zeta-Dependent Mutagenesis: Molecular Specificity, Extent of Error-Prone Synthesis, and the Role of dNTP Pools , Olga V. Kochenova

Defining the Role of Phosphorylation and Dephosphorylation in the Regulation of Gap Junction Proteins , Hanjun Li

Molecular Mechanisms Regulating MYC and PGC1β Expression in Colon Cancer , Jamie L. McCall

Pancreatic Cancer Invasion of the Lymphatic Vasculature and Contributions of the Tumor Microenvironment: Roles for E-selectin and CXCR4 , Maria M. Steele

Altered Levels of SOX2, and Its Associated Protein Musashi2, Disrupt Critical Cell Functions in Cancer and Embryonic Stem Cells , Erin L. Wuebben

Theses/Dissertations from 2015 2015

Characterization and target identification of non-toxic IKKβ inhibitors for anticancer therapy , Elizabeth Blowers

Effectors of Ras and KSR1 dependent colon tumorigenesis , Binita Das

Characterization of cancer-associated DNA polymerase delta variants , Tony M. Mertz

A Role for EHD Family Endocytic Regulators in Endothelial Biology , Alexandra E. J. Moffitt

Biochemical pathways regulating mammary epithelial cell homeostasis and differentiation , Chandrani Mukhopadhyay

EPACs: epigenetic regulators that affect cell survival in cancer. , Catherine Murari

Role of the C-terminus of the Catalytic Subunit of Translesion Synthesis Polymerase ζ (Zeta) in UV-induced Mutagensis , Hollie M. Siebler

LGR5 Activates TGFbeta Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

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Genetic dissection of the role of CBL-family ubiquitin ligases and their associated adapters in epidermal growth factor receptor endocytosis , Gulzar Ahmad

Strategies for the identification of chemical probes to study signaling pathways , Jamie Leigh Arnst

Defining the mechanism of signaling through the C-terminus of MUC1 , Roger B. Brown

Targeting telomerase in human pancreatic cancer cells , Katrina Burchett

The identification of KSR1-like molecules in ras-addicted colorectal cancer cells , Drew Gehring

Mechanisms of regulation of AID APOBEC deaminases activity and protection of the genome from promiscuous deamination , Artem Georgievich Lada

Characterization of the DNA-biding properties of human telomeric proteins , Amanda Lakamp-Hawley

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Epac interaction with the TGFbeta PKA pathway to regulate cell survival in colon cancer , Meghan Lynn Mendick

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Deconvolution of the phosphorylation patterns of replication protein A by the DNA damage response to breaks , Kerry D. Brader

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The role of ERK 1/2 signaling in the dna damage-induced G2 , Ryan Kolb

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Structural and biophysical analysis of a human inosine triphosphate pyrophosphatase polymorphism , Peter David Simone

Functions and regulation of Ron receptor tyrosine kinase in human pancreatic cancer and its therapeutic applications , Yi Zou

Theses/Dissertations from 2011 2011

Coordinate detection of new targets and small molecules for cancer therapy , Kurt Fisher

The role of c-Myc in pancreatic cancer initiation and progression , Wan-Chi Lin

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breast cancer PhD Projects, Programmes & Scholarships

Development of a multi-gene detection model for chinese luminal-type early breast cancer and its clinical translational study on the predictive value of breast cancer recurrence risk, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

MSc By Research: Exploring how the immune microenvironment contributes to outcome in male breast cancer

Self-funded phd students only.

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Characterisation of novel therapeutic approaches against invasive breast cancer

Navigating barriers to breast cancer care in conflict-affected areas of the middle east and north africa (mena): a mixed-methods study, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Development of A Next-Generation Multimodal Artificial Intelligence Platform for Early Breast Cancer Diagnosis

Cancer: understanding the immunosuppressive role of fibroblast and macrophages in breast cancer, cancer: characterization of egfr signaling activated by the endothelium in the process of breast cancer metastasis to the brain, unraveling the impact of epigenetic and microenvironmental factors on breast cancer development and progression, developing novel therapeutic strategies to better treat triple negative breast cancer, cancer: repurposing drugs as new treatments for breast cancer, msc by research: can ptp1b inhibition rewire macrophage function to improve anti-tumour immunity in breast cancer treatment, understanding the link between fibrosis, inflammation, and breast cancer initiation, assessing the molecular mechanisms by which cathepsin v promotes breast cancer growth and metastasis, development and application of recombinant antibodies to map pathology-related changes in glycosaminoglycans on breast cancer cells, funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Aberrant bone morphogenetic protein signalling in precision medicine of targeting tyrosine receptor kinase in solid tumours

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Key searches, alumni spotlight: meet iona cheng, phd, mph, building the evidence base in cancer research for asian americans.

(Photo courtesy Iona Cheng)

Iona Cheng, PhD, MPH, is co-leading a new study to uncover the causes of cancer in Asian Americans. Through a $12.45 million grant from the National Cancer Institute, she hopes to uncover the burden of cancer in this understudied group. Cheng received her PhD in epidemiology from the Department of Population and Public Health Sciences at the Keck School of Medicine of USC in 2005. Today, the cancer and genetic epidemiologist holds several positions including Professor of Epidemiology and Biostatistics at the University of California, San Francisco, Head of the Cancer Epidemiology Division, and Co-Investigator of the SEER Greater Bay Area Cancer Registry. “I am grateful for the training I received at USC because I learned to be curious and was taught rigorous applied epidemiologic skills that I apply to new research questions throughout my academic career,” shares Cheng.

What inspired you to pursue a career in epidemiology?

While today there is more awareness and opportunities for people to understand what epidemiology is, when I was in college there really wasn’t. After I completed my undergraduate studies, I stumbled on a course in epidemiology and enjoyed it. I gravitated towards the concept of public health and was drawn to understanding the causes of diseases at the population level versus focusing on a specific individual. I really appreciated how far-reaching epidemiology could be in terms of trying to understand health in the public at large.

Can you share with us more about your academic journey?

I received my bachelor’s degree in physiology from the University of California, Davis. After I graduated, I worked in the pharmaceutical industry for five years. I attended Yale University and received my Master of Public Health degree in Chronic Disease Epidemiology. It was during this time I decided to pursue a PhD degree. I wanted to study cancer epidemiology and understand differences across racial and ethnic populations. While sharing my interests with my advisor, she recommended looking beyond public health schools and advised me to look up the faculty at USC in the Department of Population and Public Health Science (formerly Preventive Medicine), and she was absolutely right! There was amazing research being done and several faculty had similar overlapping interests to mine. I was grateful for the recommendation because the department was not on my radar as my focus was on public health schools.

What sparked your interest in cancer research?

There’s such a personal story that people experience with cancer, and unfortunately it affects so many people. My underlying focus has always been trying to understand why a specific racial and ethnic population has a higher burden of cancer than others. I started out researching the high burden of prostate cancer in African America men. Then my interest expanded to breast, lung, and colorectal cancers.

My training at USC encouraged me to be curious and while I was formally trained in genetic epidemiology, my interests have expanded to different exposures. Today, I investigate geospatial exposures regarding neighborhood environments and environmental pollutants. I am also incorporating molecular elements to my research questions.

What was the most impactful aspect of your educational experience at USC?

I was really fortunate to have been trained by [the late] Dr. Brian Henderson and Dr. Malcolm Pike, and Dr. Anna Wu and Dr. Daniel Stram were a part of my dissertation committee. One highlight of my studies was learning about various epidemiological study designs while working with data from the Multiethnic Cohort Study—an amazing epidemiological resource. The study is based at several universities, which provided me with the opportunity to develop collaborations with other investigators, work in a large team, expand my network, and prepare me for the work I do today. As a result of my involvement, I had the opportunity to go to Boston to MIT to conduct genotyping work and laboratory training for my PhD dissertation. It was an enriching experience.

What informed your decision to pursue a career in academia?

I was motivated to continue my career in research and academia because I enjoyed designing studies and collaborating with others to ask complex research questions.  I currently co-lead the Disparities, Research, Environment and Multi-omics (DREAM) lab with Drs. Scarlett Lin Gomez and Salma Shariff-Marco. We have a team of approximately 30 staff, including students and postdocs working on a portfolio of about 35 research projects largely focused on cancer health disparities and inequities. We also run the Greater Bay Area Cancer Registry, where we are responsible for the collection of cancer cases across nine counties, and other cancer surveillance projects affiliated with the registry. I’ve been fortunate to work with great teams in this line of work.

How would you say that your research has evolved?

My research has evolved to expand the research questions we ask across multiple domains. We are always trying to address questions and gaps that we see in the field and adding new layers of data to understand differences in cancer risk across population groups. I started out largely focused on genetic factors and biomarkers and have expanded to studying the neighborhood environment and environmental pollutants, with an emphasis on structural and social determinants of health. I hope in the future to incorporate elements of policy for a comprehensive approach to understand all the contributing factors.

We are thrilled about our new opportunity to establish the first Asian American cohort study of cancer etiology that is funded by the National Cancer Institute. This new cohort will address important questions in understanding cancer risk among specific Asian American ethnicities. Typically, Asian Americans have been aggregated into a collective group along with Native Hawaiian and Pacific Islanders groups.

What do you enjoy about your current role at the University of California, San Francisco?

I am energized from working with our study team. We collectively bring complimentary expertise and learn from one other. The collaboration of students, trainees and staff comprising of interviewers, biostatisticians, GIS analysts, epidemiologists, and program managers – makes for stronger research questions that we can answer together. Part of our success is having the same goal and dedication in trying to understand differences in cancer risk across population groups.

What advice do you have for students who want to follow in your footsteps?

When it comes to conducting research: always be curious, learn from your experiences, and be open to asking new questions. While academia and obtaining your own research funding is challenging, I have found that being persistent has helped me continue on this journey. Lastly, I’ve been very fortunate to have had wonderful mentorship and worked with great teams, and good collaborators are also key.

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In this week's episode, Wits student Harry Saner makes history at the 2024 Paris Olympics as South Africa's first Olympic fencer since 2008, and PhD student Pedro Ornelas wins the John Kiel Scholarship. Students and staff from the Kelvin-Alexandra-Frankenwald City Studio address socio-spatial challenges in Alex and Bath Spa University funds a three-year scheme to support strategic university collaboration. Wits Chancellor Dr Judy Dlamini hosted a seminar on the global digital success of a start-up. Read more at www.wits.ac.za/news/

Wits PhD student wins prestigious John Kiel Scholarship

Wits PhD student, Pedro Ornelas, has won the prestigious John Kiel Scholarship from the International Society for Optics and Photonics (SPIE). Ornelas, a student in the Wits Structured Light Laboratory at the School of Physics won the scholarship for his work on new topological features embedded within entangled states of quantum structured light, under Professor Andrew Forbes. Ornelas’ US$ 10 000 (about R190 000) scholarship will enable him to further his research in tailoring features of classical structured light (lasers) and quantum light sources in ways to make them more robust to environmental disturbances.

Our Impact For Good

Our research output has doubled in the past five years and offers new ways to impact society for good, as well as astounding ‘moonshot moments’ that give us hope and inspiration. #WitsForGood . At Wits, we produce publications of the highest quality and we work hard to sustain the interplay between teaching and research.

National Research Foundation (NRF) rated researchers of whom 26 are A-rated.

of Wits authored publications are international journals.

Increase in Wits Research output over the past five years and 72% over the last 10 years.

Specialised research entities are active in Wits.

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Materials Science and Engineering Recognition Ceremony 2024

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