Submission information
Submission Number: 143
Submission ID: 259
Submission UUID: 7113524b-bf45-4a74-924c-499cef5d4164
Submission URI: /form/project
Created: Wed, 03/09/2022 - 22:21
Completed: Wed, 03/09/2022 - 22:21
Changed: Tue, 01/10/2023 - 13:45
Remote IP address: 71.190.115.154
Submitted by: Binlin Wu
Language: English
Is draft: No
Webform: Project
Project Title | Developing deep learning algorithms to analyze Raman spectral data for brain cancer diagnosis |
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Program | CAREERS |
Project Image | |
Tags | gpu (80), image-processing (299), machine-learning (272), neural-networks (435), python (69) |
Status | Halted |
Project Leader | Binlin Wu |
wub1@southernct.edu | |
Mobile Phone | |
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Mentor(s) | |
Student-facilitator(s) | Mit Patel |
Mentee(s) | |
Project Description | Raman spectroscopy is an optical molecular diagnostic technique that can be used for label-free in situ non-invasive cancer diagnosis. The traditional methods to analyze Raman spectra are mainly based on the intensities of characteristic peaks that are related to underlying biochemicals. However, due to the high dimensionality, and the complexity of the spectral profiles, it is often difficult and subjective to perform the analysis using the traditional methods and distinguish the spectra for different types of tissues. Using machine learning and deep learning methods to analyze the spectra and classify cancerous tissues can overcome these difficulties. The goal of the project is to use deep learning algorithms such as convolutional neural networks to analyze Raman spectra and distinguish human brain cancers at different grades and normal brain tissues. In the project, we will evaluate different methods to process Raman spectra. For example, we will evaluate different analysis methods such as classification of the spectra with and without pre-processing. |
Project Deliverables | Working code for the spectral analysis and reasonably good classification outcome using convolutional neural network (CNN). |
Project Deliverables | |
Student Research Computing Facilitator Profile | |
Mentee Research Computing Profile | |
Student Facilitator Programming Skill Level | Practical applications |
Mentee Programming Skill Level | |
Project Institution | Southern CT State University |
Project Address | 501 Crescent Street New Haven, Connecticut. 06515 |
Anchor Institution | CR-Yale |
Preferred Start Date | 03/15/2022 |
Start as soon as possible. | Yes |
Project Urgency | Already behind3Start date is flexible |
Expected Project Duration (in months) | 2 |
Launch Presentation | |
Launch Presentation Date | 04/13/2022 |
Wrap Presentation | |
Wrap Presentation Date | 05/11/2022 |
Project Milestones |
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Planned Publications (if any) | |
What will the student learn? | Parallel computing, HPC system |
What will the mentee learn? | |
What will the Cyberteam program learn from this project? | |
HPC resources needed to complete this project? | SDSC Expanse Projects Storage, SDSC Dell Cluster with NVIDIA V100 GPUs NVLINK and HDR IB (Expanse GPU). Requested through XSEDE portal with application# BIO220038. |
Notes | Student for this project is not being paid by the CAREERS program. |
What is the impact on the development of the principal discipline(s) of the project? | |
What is the impact on other disciplines? | |
Is there an impact physical resources that form infrastructure? | |
Is there an impact on the development of human resources for research computing? | |
Is there an impact on institutional resources that form infrastructure? | |
Is there an impact on information resources that form infrastructure? | |
Is there an impact on technology transfer? | |
Is there an impact on society beyond science and technology? | |
Lessons Learned | |
Overall results |