for internal use only / copyright © siemens ag 2006. all rights reserved. multiple-instance...

For internal use only / Copyright © Siemens AG 2006. All rights reserved.

Multiple-instance learning improves CAD detection of masses in digital mammographyBalaji Krishnapuram, Jonathan Stoeckel, Vikas Raykar, Bharat Rao, Philippe Bamberger, Eli Ratner, Nicolas Merlet, Inna Stainvas, Menahem Abramov, and Alexandra Manevitch

CAD and Knowledge Solutions (IKM CKS),Siemens Medical Solutions Inc., Malvern PA 19355, USA

Siemens Computer Aided Diagnosis Ltd., Jerusalem, Israel

July-22, 2008 IWDM 2008Vikas Raykar

Outline of the talk

1. CAD as a classification problem

2. Problems with off-the-shelf algorithms

3. Multiple instance learning

4. Proposed algorithm

5. Results

6. Conclusions


Typical CAD architecture

Candidate Generation

Feature Computation

Classification

Mammogram

Location of lesions

Focus of the current talk


Traditional classification algorithms

region on a mammogram lesion not a lesion

Various classification algorithms

Neural networksSupport Vector MachinesLogistic Regression ….

Often violated in CAD

Make two key assumtions

(1) Training samples are independent (2) Maximize classification accuracy over all candidates


Violation 1: Training examples are correlated

Candidate generation produces a lot of spatially adjacent candidates.

Hence there are high level of correlations.

Also correlations exist across different images/detector type/hospitals.

Proposed algorithm can handle correlations.


Violation 2: Candidate level accuracy is not important

Several candidates from the CG point to the same lesion in the breast.

Lesion is detected if at least one of them is detected.

It is fine if we miss adjacent overlapping candidates.

Hence CAD system accuracy is measured in terms of per lesion/image/patient sensitivity.

So why not optimize the performance metric we use to evaluate our system?

Most algorithms maximize classification accuracy.Try to classify every candidate correctly.

Proposed algorithm can optimize per lesion/image/patient sensitivity.


Proposed algorithm

Specifically designed with CAD in mind:

Can handle correlations among training examples.

Optimizes per lesion/image/patient sensitivity.

Joint classifier design and feature selection.

Selects accurate sparse models.

Very fast to train and no tunable parameters.

Developed in the framework of multiple-instance learning.


Outline of the talk


2. Problems with off-the-shelf algorithms Assume training examples are independent. Minimize classification accuracy.

3. Multiple instance learning

4. Algorithm summary

5. Results

6. Conclusions


Multiple Instance Learning

How do we acquire labels ?

Candidates which overlap with the radiologist mark is a positive.Rest are negative.

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Single Instance Learning

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Multiple Instance Learning

Classify every candidate correctly

Positive Bag

Classify at-least one candidate correctly


Simple Illustration

Single instance learning:

Reject as many negative candidates as possible.

Detect as many positives as possible.

Multiple Instance LearningSingle Instance Learning

Multiple instance learning:

Reject as many negative candidates as possible.

Detect at-least one candidate in a positive bag.


Outline of the talk


2. Problems with off-the-shelf algorithms Assume training examples are independent. Minimize classification accuracy.

3. Multiple instance learning Notion of positive bags A bag is positive if at-least one instance is positive.

4. Algorithm summary

5. Results

6. Conclusions


Algorithm Details

Logistic Regression model

feature vectorweight vector


Maximum Likelihood Estimator


Prior to favour sparsity

If we know the hyperparameters we can find our desired solution.

How to choose them?.


Feature Selection


Outline of the talk


2. Problems with off-the-shelf algorithms Assume training examples are independent.

Minimize classification accuracy.

3. Multiple instance learning Notion of positive bags

A bag is positive if at-least one instance is positive.

4. Algorithm summary Joint classifier design and feature selection.

Maximizes the performance metric we care about.

5. Results


Datasets used

Training set

144 biopsy proven malignant-mass cases.

2005 normal cases from BI-RADS 1 and 2 category.

Validation set

108 biopsy proven malignant-mass cases.

1513 normal cases from BI-RADS 1 and 2 category.


Patient level FROC curve for the validation set

Proposed methodis more accurate


MIL selects much fewer features

Total number of features 81

Proposed MIL algorithm 40

Proposed algorithm without MIL 56


Patient vs Candidate level FROC curve

Improves per-patient FROC at the cost of deteriorating per-candidate FROC

Message: Design algorithms to optimize the metric you care about.


Conclusions

A classifier which maximzes the performance metric we care about.

Selects sparse models.

Very fast. Takes less than a minute to train for over 10,000 patients.

No tuning parameters.

Improves the patient level FROC curves substantially.


Questions / Comments?

for internal use only / copyright © siemens ag 2006. all rights reserved. multiple-instance...

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vikas raykar violation

vikas raykar outline

multipleinstance learning

classification accuracy

classification problem

cad system accuracy

single instance learning

proposed algorithm