Deep learning for biomedical image analysis

Submission Number: 64
Submission ID: 95
Submission UUID: 4888f4de-f778-4bec-b419-91e292a9632f
Submission URI: /form/project

Created: Fri, 08/14/2020 - 23:33
Completed: Fri, 08/14/2020 - 23:33
Changed: Tue, 08/02/2022 - 14:58

Remote IP address: 104.162.133.66
Submitted by: Binlin Wu
Language: English

Is draft: No
Webform: Project
Deep learning for biomedical image analysis
CAREERS
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ai (271), big-data (4), deep-learning (303), gpu (80), machine-learning (272), matlab (2), python (69), tensorflow (51)
Complete

Project Leader

Binlin Wu
917-292-6794
6465938848

Project Personnel

Neil McGlohon, Jason Smith
Andrew Cheng
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Project Information

In this project, we will attempt to use convolutional neural networks (CNNs) deep learning methods to analyze and classify fluorescence images collected from two different types of kidney tumor tissue slides.

Project Information Subsection

If successful, this project will develop a set of parameterized CNNs able to analyze a set of images and achieve high classification accuracy.
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Upper level undergrad or grade, physics or computer science students with programming background.
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Practical applications
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Southern Connecticut State University
501 Crescent Street, ASB/SCI 045
New Haven, Connecticut. 06515
CR-Yale
10/01/2020
No
Already behind3Start date is flexible
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03/10/2021
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05/12/2021
  • Milestone Title: Milestone 1
    Milestone Description: Adopt a model and make it run on a GPU
    Completion Date Goal: 2020-11-01
  • Milestone Title: Milestone 2
    Milestone Description: Optimize the model for the image set
    Completion Date Goal: 2020-12-01
  • Milestone Title: Milestone 3
    Milestone Description: Apply the model to classify images and produce results. Give brief wrap-up presentation.
    Completion Date Goal: 2021-02-01
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Publication is expected if good results are achieved.
Cancer diagnosis, optical imaging, biomedical image analysis, deep learning, GPU usage
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GPU-equipped nodes
Interested in using AiMOS at Rensselaer

Final Report

This CAREERS project has made a significant impact on my research medical image analysis. The implementation of parallel computing using an HPC system is crucial in the project. Since the program and code developed in the project can also be used in other similar projects that involve image analysis, the impact the project has made is not limited to this project, but on a much broader area.
This research is highly interdisciplinary. The problem we are dealing with is essentially common in any area where researchers study a substance based on image analysis. The work done in this project will definitely make an impact on other disciplines. The development of such technologies of image analysis using deep learning can be used in biomedical areas such as cancer diagnosis, remote sensing such as radar or lidar technologies, biometric analysis, nanotechnology for material characterization, astronomical research, etc.
The researchers including myself who have participated in the program have learned a lot through this program, such as what resources are available in this region, experts we can seek help from, and the mechanism of the program. The successful experience from this program will inspire us to acquire physical resources in our own institution to support research. With collaboration, the available resources we have learned might help us to develop such infrastructure.
Through this research, the students have been trained to be research facilitators to help researchers in different areas to perform crucial modeling and computing tasks in their research. The students got trained not only in the computing techniques using HPC but also learned how to work with researchers to implement computations to solve a scientific problem. It has definitely made a significant impact on the development of human resources for research computing.
The successful experience of the student and the researcher will be a great demonstration to my colleagues and the administration, which may inspire my institution to arrange resources to support the development of infrastructure.
Yes, as I have mentioned above, the program provided great information resources to the students and the researchers, who can play important roles to form infrastructure.
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The research is highly interdisciplinary. It covers multiple areas such as cancers, optical imaging, image processing, statistical analysis, artificial intelligence etc. The technology being developed can be transferred to many other areas with important applications and make an impact on society beyond science and technology.
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I think the overall results of the project are outstanding. The program itself and the trained research facilitators are trying to fill the gap between the computing community and the general research community in which intensive computation plays a crucial role both in the short term and in the long term. Through the project, the researchers and the students all gained a lot from the program. The research (myself) can get the project accomplished with great resources as support including the computing experts and the physical resources. In the meantime, the information of the resources is another beneficial part to the researchers. The program provides an excellent learning opportunity to the student trainees with the support of mentors, physical resources, and networking opportunities, which can be an eye-opening or even life-changing opportunity. They have a chance to learn about computing techniques especially using HPC systems, performing computing tasks in scientific research, and even career paths that they have never thought about before. All these they have gained from the program can make a great impact on their future career.