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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
Page 2 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
Page 3 July-22, 2008 IWDM 2008Vikas Raykar
Typical CAD architecture
Candidate Generation
Feature Computation
Classification
Mammogram
Location of lesions
Focus of the current talk
Page 4 July-22, 2008 IWDM 2008Vikas Raykar
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
Page 5 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 6 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 7 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 8 July-22, 2008 IWDM 2008Vikas Raykar
Outline of the talk
1. CAD as a classification problem
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
Page 9 July-22, 2008 IWDM 2008Vikas Raykar
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
Page 10 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 11 July-22, 2008 IWDM 2008Vikas Raykar
Outline of the talk
1. CAD as a classification problem
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
Page 12 July-22, 2008 IWDM 2008Vikas Raykar
Algorithm Details
Logistic Regression model
feature vectorweight vector
Page 13 July-22, 2008 IWDM 2008Vikas Raykar
Maximum Likelihood Estimator
Page 14 July-22, 2008 IWDM 2008Vikas Raykar
Prior to favour sparsity
If we know the hyperparameters we can find our desired solution.
How to choose them?.
Page 15 July-22, 2008 IWDM 2008Vikas Raykar
Feature Selection
Page 16 July-22, 2008 IWDM 2008Vikas Raykar
Feature Selection
Page 17 July-22, 2008 IWDM 2008Vikas Raykar
Outline of the talk
1. CAD as a classification problem
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
Page 18 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 19 July-22, 2008 IWDM 2008Vikas Raykar
Patient level FROC curve for the validation set
Proposed methodis more accurate
Page 20 July-22, 2008 IWDM 2008Vikas Raykar
MIL selects much fewer features
Total number of features 81
Proposed MIL algorithm 40
Proposed algorithm without MIL 56
Page 21 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 22 July-22, 2008 IWDM 2008Vikas Raykar
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.
Page 23 July-22, 2008 IWDM 2008Vikas Raykar
Questions / Comments?