Towards Automated Melanoma Detection with Deep …...Towards Automated Melanoma Detection with Deep...
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Motivation Related work Proposed approach Empirical results Conclusion Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation Devansh Bisla, Anna Choromanska, Russell S. Berman, Jennifer A. Stein, David Polsky New York University, New York, NY, USA Code: https://bit.ly/2KFRp5e Paper: https://bit.ly/2FBgOZP
Transcript of Towards Automated Melanoma Detection with Deep …...Towards Automated Melanoma Detection with Deep...
Motivation Related work Proposed approach Empirical results Conclusion
Towards Automated Melanoma Detectionwith Deep Learning:
Data Purification and Augmentation
Devansh Bisla, Anna Choromanska, Russell S. Berman,Jennifer A. Stein, David Polsky
New York University, New York, NY, USA
Code: https://bit.ly/2KFRp5e Paper: https://bit.ly/2FBgOZP
Motivation Related work Proposed approach Empirical results Conclusion
Motivation
Figure 1: (Left) Data Imbalancedness (Right) Data Impurities
Motivation Related work Proposed approach Empirical results Conclusion
Existing computational techniques
Traditional machine learningHand-crafted extraction of features from the data such as
Lesion Symmetry/Asymmetry.Irregular borders.Non-Uniform pigmentation.Lesion size.
Problem: not scalable to large data sets.
Deep Learning
Automatically extract features from large sized data.Problem: Needs large, balanced, and unbiased data.
Motivation Related work Proposed approach Empirical results Conclusion
Traditional training
Visualization results for the conventionally-trained model (Top): Originalimage. (Bottom): Visualization mask overlaid on the original image.
The model overfits to image occlusions such as hairs, rulersand ink marks.
Motivation Related work Proposed approach Empirical results Conclusion
Proposed approach
Data Impurities:
Removal of unwanted objects such as hair, rulers etc.
Data Imbalancedness
Synthetic data generation.Data augmentation.
Motivation Related work Proposed approach Empirical results Conclusion
Data purification
Thresholding in the LUV color space combined withmorphological operations. Note that this may also removedark regions belonging to the lesion itself.[PhilippeSchmid-Saugeon et al]
Overlay the processed image with the segmented lesionobtained from our segmentation algorithm.
Motivation Related work Proposed approach Empirical results Conclusion
Data purification - results
(a) (b) (c) (d) (e)
Figure 2: Top: Original images. Bottom: Images obtained after a,b)scales, c) hairs and scales, and d,e) hairs removal.
Motivation Related work Proposed approach Empirical results Conclusion
Data generation
Figure 3: Architecture of Generative Adversarial Network
Main idea:
Train a generator network to generate images which havesimilar distribution to the one followed by the training data,but do not appear in the training data set.
The discriminator provides a feedback on similarity betweenthe two distributions.
We generated 350 images of melanoma and 750 images ofseborrheic keratosis.
Motivation Related work Proposed approach Empirical results Conclusion
Data generation - results
0.02 0.04 0.06 0.08 0.10Mean Squared Error
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Histogram for Seborrheic Keratosis
0.0 0.1 0.2 0.3Mean Squared Error
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Histogram for Melanoma
Figure 4: Histograms of the MSE values for (left) seborrheic keratosisand (right) melanoma.
Motivation Related work Proposed approach Empirical results Conclusion
Data generation - results
Seborrheic Keratosis
0.02 0.04 0.059
Melanoma
0.02 0.09 0.18
Motivation Related work Proposed approach Empirical results Conclusion
Classification results: confusion matrix
M N SK
Predicted label
M
N
SK
Tru
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80 19 18
89 269 35
12 6 72
Confusion matrix
M N SK
Predicted label
M
N
SK
Tru
e label
83 23 11
38 338 17
6 15 69
Confusion matrix
Figure 5: Confusion matrix obtained by traditional baseline (left) andproposed model (right).
Motivation Related work Proposed approach Empirical results Conclusion
Classification results: ROC-AUC
Mean Value ROC-AUC
Our Approach 0.915Kazuhisa Matsunaga[K. Matsunaga et al.] 0.911
RECOD Titans[A. Menegola et al.] 0.908
Table 1: Leader-board for melanoma and seborrheic keratosis combined.
Method 82% 89% 95%
Top AVG[K. Matsunaga et al.] 0.729 0.588 0.366
Top SK [I. Gonzalez Diaz et al.] 0.727 0.555 0.404
Top M [A. Menegola et al.] 0.747 0.590 0.395
Our Approach 0.697 0.648 0.492
Table 2: Specificity values at sensitivity levels of 82%/89%/95% formelanoma classification. Top AVG, Top SK, and Top M denote thewinning approaches of the ISIC 2017 challenge.
Motivation Related work Proposed approach Empirical results Conclusion
Classification results visualized
TP FP FN TN
Figure 6: Visualization results for seborrheic keratosis. Top: Originalimage. Bottom: Visualization result.
Motivation Related work Proposed approach Empirical results Conclusion
Classification results visualized
TP FP FN TN
Figure 7: Visualization results for Nevus. Top: Original image. Bottom:Visualization result.
Motivation Related work Proposed approach Empirical results Conclusion
Conclusion
Deep learning based methods are the most accurate andscalable, but they require large, pure and balanced trainingdata sets.
We presented solutions to improve effectiveness ofclassification systems by data purification (removal ofunwanted objects) and data augmentation (synthetic datageneration).
