Implementation of InfoGAN Neural Network for Breast Cancer Cell Clustering

infoganJL: 

Julia implementation of InfoGAN neural

network for semi-supervised clustering of

breast cancer cells

Christian Landeros

Harvard-MIT Program in Health Sciences

and Technology

Center for Systems Biology,

Massachusetts General Hospital

OVERVIEW

•

Problem Motivation

•

InfoGAN Background

•

Implementation Design & Results

OVERVIEW

•

Problem Motivation

•

InfoGAN Background

•

Implementation Design & Results

Problem Motivation

Breast Cancer Biopsy Analysis

•

Presence of key hormone and growth factor

receptors on breast cancer cells inform clinical

decision-making

Problem Motivation

Breast Cancer Biopsy Analysis

•

Presence of key hormone and growth factor

receptors on breast cancer cells inform clinical

decision-making

•

For low-resource settings, time-consuming and

costly biopsy analysis methods create 

diagnostic

bottlenecks

Tsu, V. D., Jeronimo, J., & Anderson, B. O. (2013). Why the time is right to tackle breast and cervical

cancer in low-resource settings. Bulletin of the World Health Organization, 91, 683-690.

Problem Motivation

Digital Holography for Low-Cost Molecular Data Aquisition

Digital Holography

Bright-field

Image

Computational

Reconstruction

Problem Motivation

Digital Holography for Low-Cost Molecular Data Aquisition

Digital Holography

Bright-field

Image

Computational

Reconstruction

Not very feasible for low-

resource settings

Problem Motivation

Breast Cancer Biopsy Analysis

Single-cell Cut-outs

size = (64,64,2)

OVERVIEW

•

Problem Motivation

•

InfoGAN Background

•

Implementation Design & Results

InfoGAN Background

Classification by Generative Adversarial Networks

•

Key assumption

:

Any data sample is the end result of a generative process where complex interactions

from many independent factors generate data variability

Single cell under imaged

by digital holography

InfoGAN Background

Classification by Generative Adversarial Networks

•

Key assumption

:

Any data sample is the end result of a generative process where complex interactions

from many independent factors generate data variability

…

…

“background illumination”

“number of cells”

“red coloration”

“ovalness of cell #1”

E

x

a

m

p

l

e

:

InfoGAN Background

Classification by Generative Adversarial Networks

•

Key assumption

:

Any data sample is the end result of a generative process where complex interactions

from many independent factors generate data variability

…

…

“background illumination”

“number of cells”

“red coloration”

“ovalness of cell #1”

E

x

a

m

p

l

e

:

Features that we

care about and

want to learn

InfoGAN Background

Classification by Generative Adversarial Networks

InfoGAN Background

Classification by Generative Adversarial Networks

InfoGAN Background

Classification by Generative Adversarial Networks

InfoGAN Background

Classification by Generative Adversarial Networks

We want to minimize “entropy” of

this distribution to so that 

c

provides maximum information

OVERVIEW

•

Problem Motivation

•

InfoGAN Background

•

Implementation Design & Results

Implementation Design & Results

Network Architectures

Implementation Design & Results

Network Architectures

Julia implementation of InfoGAN neural network for semi-supervised clustering of breast cancer cells. The project aims to address diagnostic bottlenecks in low-resource settings by utilizing digital holography for molecular data acquisition. By analyzing key hormone and growth factor receptors on breast cancer cells, the system informs clinical decision-making in a cost-effective and efficient manner.

• Breast Cancer
• InfoGAN
• Neural Network
• Clustering
• Digital Holography

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Presentation Transcript

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1. infoganJL: Julia implementation of InfoGAN neural network for semi-supervised clustering of breast cancer cells Christian Landeros Harvard-MIT Program in Health Sciences and Technology Center for Systems Biology, Massachusetts General Hospital

2. OVERVIEW Problem Motivation InfoGAN Background Implementation Design & Results

3. OVERVIEW Problem Motivation InfoGAN Background Implementation Design & Results

4. Problem Motivation Breast Cancer Biopsy Analysis Presence of key hormone and growth factor receptors on breast cancer cells inform clinical decision-making

5. Problem Motivation Breast Cancer Biopsy Analysis Presence of key hormone and growth factor receptors on breast cancer cells inform clinical decision-making For low-resource settings, time-consuming and costly biopsy analysis methods create diagnostic bottlenecks Tsu, V. D., Jeronimo, J., & Anderson, B. O. (2013). Why the time is right to tackle breast and cervical cancer in low-resource settings. Bulletin of the World Health Organization, 91, 683-690.

6. Problem Motivation Digital Holography for Low-Cost Molecular Data Aquisition Computational Reconstruction Digital Holography Bright-field Image

7. Problem Motivation Digital Holography for Low-Cost Molecular Data Aquisition Not very feasible for low- resource settings Computational Reconstruction Digital Holography Bright-field Image

8. Problem Motivation Breast Cancer Biopsy Analysis Single-cell Cut-outs size = (64,64,2)

9. OVERVIEW Problem Motivation InfoGAN Background Implementation Design & Results

10. InfoGAN Background Classification by Generative Adversarial Networks Single cell under imaged by digital holography Key assumption: Any data sample is the end result of a generative process where complex interactions from many independent factors generate data variability

11. InfoGAN Background Classification by Generative Adversarial Networks Example: background illumination number of cells red coloration ovalness of cell #1 Key assumption: Any data sample is the end result of a generative process where complex interactions from many independent factors generate data variability

12. InfoGAN Background Classification by Generative Adversarial Networks Example: background illumination Features that we care about and want to learn number of cells red coloration ovalness of cell #1 Key assumption: Any data sample is the end result of a generative process where complex interactions from many independent factors generate data variability

13. InfoGAN Background Classification by Generative Adversarial Networks

14. InfoGAN Background Classification by Generative Adversarial Networks

15. InfoGAN Background Classification by Generative Adversarial Networks

16. InfoGAN Background Classification by Generative Adversarial Networks We want to minimize entropy of this distribution to so that c provides maximum information

17. OVERVIEW Problem Motivation InfoGAN Background Implementation Design & Results

18. Implementation Design & Results Network Architectures

19. Implementation Design & Results Network Architectures