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#SIIM17
The Impact of Segmentation on the
Accuracy and Sensitivity of a
Melanoma Classifier
Based on Skin Lesion Images
Og...
#SIIM17
Our Team
Adrià Romero López Xavier Giró-i.Nieto
Image Processing Group
Signal Theory and
Communications Department...
#SIIM17
Outline
• Motivation
• Context
• Scope and goals
• Challenges
• State of the art
• Our work
• Hypothesis
• Methodo...
#SIIM17
Motivation
[Source: https://seer.cancer.gov/statfacts/html/melan.html]
Melanoma of the skin
#SIIM17
Context
• This is not a typical SIIM presentation
• No specialized imaging equipment
• No PACS
• No DICOM
• No met...
#SIIM17
Scope
• Skin Disease: An
Illustrated Taxonomy
• Our focus: skin
lesion analysis for
(early) melanoma
detection
[So...
#SIIM17
Scope and Goals
• Scope:
• Help physicians to detect melanoma (a 2-class
classifier)
• Goals:
• Design an intellig...
#SIIM17
A Challenging Problem
8
#SIIM17
Success Rate - Sensitivity
Physicians, as low as:
• 43 % - naked eye
• 79 % - dermoscopy
(Vestergaard et al, 2008)...
#SIIM17
Classical Techniques
[Source: Center For Excellence In Dermatology - Kennewick, WA]
CASH (Henning et al., 2007)
Co...
#SIIM17
Typical workflow (pre-DL)
Source: [Glaister2013]
11
#SIIM17
Challenges
• Segmentation
• Artifacts, such as: freckles, hair, shading, and noise.
• Irregular, fuzzy lesion bord...
#SIIM17
State of the art
• Esteva et al. Nature 2017
#SIIM17
Our Approach
• Pretrained VGG16
(Simonyan & Zisserman, 2014)
• Transfer Learning
[Source:Blier 2016]
#SIIM17
Transfer Learning
Source: [http://www.slideshare.net/hunkim/transfer-defectlearningnew-completed]
#SIIM17
Transfer Learning
1. Train on
Imagenet
3. Medium dataset:
finetuning
more data = retrain more of
the network (or a...
#SIIM17
Prediction Examples:
Getting It Right
True Positives True Negatives
#SIIM17
Prediction Examples:
Getting It Wrong
False Positives False Negatives
#SIIM17
Our Hypothesis
• Image segmentation improves the performance of skin lesion
classifiers using convolutional neural...
#SIIM17
Methods
• ISBI 2016 Challenge dataset
• Skin Lesion Analysis towards melanoma detection
• 1279 RGB images
• Labele...
#SIIM17
Methods
• Dataset balancing through downsampling.
• Dataset split: 70-30% training/testing
• Input images:
• Unseg...
#SIIM17
Results
#SIIM17
Further Investigation
• What if we vary the degree of border expansion?
Sensitivity Accuracy AUC
Perfect Segmentat...
#SIIM17
Ongoing and Future Work
•Additional / larger / more challenging datasets
•Other CNN architectures
•Better image pr...
#SIIM17
Concluding remarks
• Challenges
• Difficulty in acquiring datasets and reproducing / benchmarking results
• The “b...
#SIIM17
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The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images

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Publié le

Jack Burdick, Oge Marques, Borko Furht, Janet Weinthal (FAU, Boca Raton, FL, USA), Adria Romero Lopez, and Xavier Giro-i-Nieto (UPC, Barcelona).

Presented at SIIM 2017 Annual Meeting: http://siim.org/page/17s_analytics3

Publié dans : Données & analyses
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The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images

  1. 1. #SIIM17 The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images Oge Marques, PhD Professor College of Engineering and Computer Science Florida Atlantic University
  2. 2. #SIIM17 Our Team Adrià Romero López Xavier Giró-i.Nieto Image Processing Group Signal Theory and Communications Department MIDDLE Research Group Oge Marques Borko Furht Jack Burdick Janet Weinthal NSF Award No. 1464537, I/UCRC Phase II under NSF 13-542
  3. 3. #SIIM17 Outline • Motivation • Context • Scope and goals • Challenges • State of the art • Our work • Hypothesis • Methodology • Experimental results • Ongoing and future work • Concluding remarks
  4. 4. #SIIM17 Motivation [Source: https://seer.cancer.gov/statfacts/html/melan.html] Melanoma of the skin
  5. 5. #SIIM17 Context • This is not a typical SIIM presentation • No specialized imaging equipment • No PACS • No DICOM • No metadata • No workflows • Instead... • Regular photographs • Unstructured (and purely visual) data • Minimal ground truth
  6. 6. #SIIM17 Scope • Skin Disease: An Illustrated Taxonomy • Our focus: skin lesion analysis for (early) melanoma detection [Source: Esteva et al., Nature (2017)]
  7. 7. #SIIM17 Scope and Goals • Scope: • Help physicians to detect melanoma (a 2-class classifier) • Goals: • Design an intelligent medical imaging skin lesion diagnosis system using deep learning techniques • Achieve (or improve upon) state-of-the-art results for skin lesion classification
  8. 8. #SIIM17 A Challenging Problem 8
  9. 9. #SIIM17 Success Rate - Sensitivity Physicians, as low as: • 43 % - naked eye • 79 % - dermoscopy (Vestergaard et al, 2008) Deep learning based methods, as high as: • 94.83 % (Jafari et al, 2016; Premaladha and Ravichandran 2016)
  10. 10. #SIIM17 Classical Techniques [Source: Center For Excellence In Dermatology - Kennewick, WA] CASH (Henning et al., 2007) Color Architecture Symmetry Homogeneity
  11. 11. #SIIM17 Typical workflow (pre-DL) Source: [Glaister2013] 11
  12. 12. #SIIM17 Challenges • Segmentation • Artifacts, such as: freckles, hair, shading, and noise. • Irregular, fuzzy lesion border • Low contrast between lesion and surrounding skin • Multi-fragment lesions • Classification • Manual feature extraction is not effective • False positives and false negatives have different impact
  13. 13. #SIIM17 State of the art • Esteva et al. Nature 2017
  14. 14. #SIIM17 Our Approach • Pretrained VGG16 (Simonyan & Zisserman, 2014) • Transfer Learning [Source:Blier 2016]
  15. 15. #SIIM17 Transfer Learning Source: [http://www.slideshare.net/hunkim/transfer-defectlearningnew-completed]
  16. 16. #SIIM17 Transfer Learning 1. Train on Imagenet 3. Medium dataset: finetuning more data = retrain more of the network (or all of it) 2. Small dataset: feature extractor Freeze these Train this Freeze these Train this Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy Medical Imaging case
  17. 17. #SIIM17 Prediction Examples: Getting It Right True Positives True Negatives
  18. 18. #SIIM17 Prediction Examples: Getting It Wrong False Positives False Negatives
  19. 19. #SIIM17 Our Hypothesis • Image segmentation improves the performance of skin lesion classifiers using convolutional neural networks. [Source:International Skin Imaging Collaboration Archive] Not segmented Perfectly segmented Partially segmented
  20. 20. #SIIM17 Methods • ISBI 2016 Challenge dataset • Skin Lesion Analysis towards melanoma detection • 1279 RGB images • Labeled as either benign or malignant Class Benign Malignant Total Images Training subset 727 173 900 Testing subset 304 75 379
  21. 21. #SIIM17 Methods • Dataset balancing through downsampling. • Dataset split: 70-30% training/testing • Input images: • Unsegmented images: straight from the dataset. • Perfectly segmented images: bitwise AND operation of the unaltered images and its corresponding binary mask provided by the ISIC dataset. • Partially segmented images: original binary masks morphologically dilated with a disk-shaped structuring element (50 pixel radius). • Additional preprocessing methods (resizing and normalization) were also performed to match the input size expected by the VGG16 architecture.
  22. 22. #SIIM17 Results
  23. 23. #SIIM17 Further Investigation • What if we vary the degree of border expansion? Sensitivity Accuracy AUC Perfect Segmentation 45.3% 58.7% 62.2% +25 53.3% 61.3% 64.2% +50 56.0% 60.7% 62.6% +75 57.3% 59.3% 60.8% +100 34.7% 55.3% 57.9% Unsegmented 24.0% 51.3% 53.2%
  24. 24. #SIIM17 Ongoing and Future Work •Additional / larger / more challenging datasets •Other CNN architectures •Better image preprocessing •Partnerships and collaborations •Mobile app
  25. 25. #SIIM17 Concluding remarks • Challenges • Difficulty in acquiring datasets and reproducing / benchmarking results • The “black box” aspect of DL-based solutions • Hard to tell positives from negatives • Learning curve: TensorFlow, Keras, HPC, DL concepts and best practices, etc. • Opportunities • Many variations of the basic classification problem • Mobile app market • Tech-minded dermatology practices
  26. 26. #SIIM17

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