Project Info
Scalble Machine Learning for Automatically Diagnosing Breast Cancer Using Large-Scale Histopathological Images
Hua Wang
huawang@mines.edu
Project Goals and Description:
Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States of America. Given that an early diagnosis is imperative to prevent breast cancer progressions, many machine learning models have been developed to automate the histopathological classification of the different types of carcinomas in recent years. In this study, we how to classify the histopathological breast cancer images and determine which tissue segments in an image exhibit an indication of using large-scale histopathological images, where we mainly address the computational efficiency of designed machine learning models.
More Information:
Grand Challenge: Advance health informatics.
The following papers are relevant and can be helpful to understand the project.
https://www.sciencedirect.com/science/article/pii/S0895611118302659
https://www.nature.com/articles/s41598-021-87644-7.pdf
Primary Contacts:
Hua Wang, huawang@mines.edu
Student Preparation
Qualifications
The applicant student is expected to have already taken CSCI 261 and 262. It would be beneficial if the student has already taken CSCI 470.
TIME COMMITMENT (HRS/WK)
6-8 hours/ week
SKILLS/TECHNIQUES GAINED
1. The students will learn the skills to perform data processing and management.
2. The students will be involved in my research team to perform research on machine learning and data mining.
3. The students will be involved in scientific paper writing for the results of this project.
4. The students will have a chance to work together with my collaborators in medical schools.
5. The students will gain fundamental knowledge on medical image computing, as well as how to use machine learning, as well as computational algorithms, to deal with problems in medical image computing.
In a word, after the training in this project by successfully completing the assigned research tasks, the student is expected to be ready for pursuing a graduate degree in the area of machine learning, data mining, artificial intelligence, or a broader area of computer science.
MENTORING PLAN
1. One orientation meeting is planned at the beginning of the project, in which the undergraduate students will be introduced to the research team. The project and research culture of the faculty’s research team will be introduced to the undergraduate students in the meeting.
2. Technical seminars within the research team are planned, once per week. In every meeting, the undergraduate students will present a research paper relevant to the project and lead discussions on it with the faculty and the graduate students in the research team.
3. Professional development sessions within the research team are planned, once per week. In every meeting, the faculty or the graduate students in the research team will examine the progress of the project and the recent research results, exchange ideas with the undergraduate students, and help them develop research skills, including algorithm development, experimental design, scientific results evaluation, paper writing, and so on.
4. A poster session will be conducted at the end of the project in which the results of this project will be presented to the research teams of the faculty, the Computer Science Department, and the collaborators of the faculty.
PREFERRED STUDENT STATUS
Freshman
Sophomore
Junior
Senior