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Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Networks

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The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.

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Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Networks

  1. 1. SKIN LESION DETECTION FROM DERMOSCOPIC IMAGES USING CONVOLUTIONAL NEURAL NETWORKS Adrià Romero López Oge Marques Xavier Giró-i.Nieto AUTHOR ADVISORS
  2. 2. Acknowledgments 2 MIDDLE Research Group Víctor Campos Albert Gil Jack Burdick Janet Weinthal Adam Lovett Oge Marques Borko Furht Xavier Giró-i.Nieto Albert Jiménez
  3. 3. ‘’ Outline 3 1. Motivation 2. State of the art 3. Methodology 4. Experimental Results 5. Conclusions
  4. 4. 1. Motivation 4
  5. 5. Background of the problem ▣ Skin cancer: most predominant type of cancer ▣ The frequency of melanoma doubles every 20 years ▣ Each year (in USA): □ 76,380 new cases of melanoma □ 6,750 deaths ▣ Melanoma is a deadly form of skin cancer, but survival rates are high if detected and diagnosed early ▣ Melanoma detection: rely on hand-crafted features □ ABCDE rule (Asymmetry, Border, Color, Dermoscopic structure, and Evolving) □ CASH rule (Color, Architecture, Symmetry, and Homogeneity) 5
  6. 6. Background of the problem ▣ Discriminating between benign and malignant skin lesions is challenging ▣ Without computer-based assistance: 60~80% detection accuracy 6
  7. 7. Scope and goals ▣ Scope: □ Assist physicians in classifying skin lesions (especially in melanoma detection: 2-class classifier problem) ▣ Goal: □ Use state-of-the-art techniques, called Deep Learning, to design an intelligent medical imaging-based skin lesion diagnosis system □ Achieve (or improve upon) state-of-the-art results for: ■ skin lesion segmentation, and ■ skin lesion classification □ Evaluate the impact of skin lesion segmentation on the accuracy of the classifier 7
  8. 8. Hypothesis Previous segmentation of an image containing a skin lesion (i.e., isolating the lesion from the background) improves the accuracy and sensitivity of a Deep Learning classification model approach.
  9. 9. Challenges ▣ Dermoscopic images may: ■ Contain artifacts, such as: moles, freckles, hair, patches, shading and noise. ■ Present low contrast images between lesion and background ■ Contain multiple skin lesions 9
  10. 10. Related work •Typical block diagram (Non-Deep Learning approach from [Glaister2013]) 10
  11. 11. 2. State of the art 11
  12. 12. State-of-the-art hierarchy 12 CNNs
  13. 13. Deep learning motivation ▣ Image representations to: □ Image classification □ Object detection and recognition □ Semantic Segmentation 13 Self-driving cars[Goodfellow et al. 2014] [Ciresan et al. 2013] [Turaga et al 2010] Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy
  14. 14. Supervised learning 14 [Car] [Dog] Parameters Slide credit: “Artificial Intelligence, revealed” by Facebook Research
  15. 15. Why deep learning now? 15 Large datasets GPUs (Graphics Processing Unit) * Not applicable to medical imaging [Deng et al. Russakovsky et al.] [NVIDIA et al.] Framework
  16. 16. Convolutional Neural Networks 16 Some input vector (our images). Also known as ConvNets or CNNs Our class label ▣ Convolutional Layers ▣ Activation Layers ▣ Pooling Layers
  17. 17. Convolution layer 17 32 32 3 5x5x3 filter 32x32x3 image Convolve the filter with the image i.e. “slide over the image spatially, computing dot products” Filters always extend the full depth of the input volume Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy
  18. 18. Convolution layer 18 32 32 3 32x32x3 image 1 number: the result of taking a dot product between the filter and a small 5x5x3 chunk of the image (i.e. 5*5*3 = 75-dimensional dot product + bias) Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy Linear function 5x5x3 filter → weights (Learnt by Backpropagation algorithms)
  19. 19. Activation layer 19 32 32 3 32x32x3 image 5x5x3 filter Convolve (slide) over all spatial locations ReLU (Rectified Linear Units) 1 28 28 Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy activation map
  20. 20. Pooling layer ▣ Undersampling task □ Makes the representation smaller and more manageable □ Operates over each activation map independently 20 Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy
  21. 21. Fully-Connected (FC) layer 21
  22. 22. Main scheme 22 Input image [Yann LeCun et al.]
  23. 23. Main scheme 23 1. Convolutional Layers 2. Activation Layer 3. Pooling Layers [Yann LeCun et al.]
  24. 24. Main scheme 24 [Yann LeCun et al.] Fully-Connected Layer
  25. 25. Main scheme 25 [Yann LeCun et al.] Output label
  26. 26. ConvNets for classification ▣ Classification → Scoring: □ The CNN computes a class score {float} to each image □ This score will be related to a class label {integer} 26 [224x224x3] f Class scores, indicating class labels training Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy
  27. 27. ConvNets for segmentation ▣ Segmentation → Localization: □ The CNN assigns a class label to each pixel (classify all pixels) ■ {0,1} → {absence of object, presence of object} □ 27 Slide credit: CS231n
  28. 28. ConvNets for segmentation 28 Slide credit: CS231n ▣ Upsampling □ From labels {1x1} to Segmented Image {224x224} px
  29. 29. Transfer learning 29 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
  30. 30. 3. Methodology 30
  31. 31. Framework ▣ Python environment: □ Keras - Deep Learning Library for Theano or TensorFlow □ OpenCV / PIL (Python Imaging Library) □ SciPy (Library for Mathematics, Science and Engineering) □ Scikit-learn (Machine Learning Library) □ CUDA library for the GPUs 31 + =
  32. 32. ISIC Archive dataset ▣ ISBI 2016 Challenge dataset □ Skin Lesion Analysis towards melanoma detection □ 1279 RGB images □ Labeled as either benign or malignant □ Includes the binary mask for each image 32 Class Benign Malignant Total Images Training subset 727 173 900 Validation subset 304 75 379 0 → outside lesion area 255 → inside lesion area Binary mask
  33. 33. Method scheme 33
  34. 34. Data augmentation ▣ Enlarge our few training examples: □ Re-scaling □ 40 degrees rotations □ Horizontal shifts □ Zooming □ Horizontal flips 34 Original image Random transformations
  35. 35. Preprocessing ▣ Mean subtraction: X -= np.mean(X, axis = 0) ▣ Image Normalization: X /= np.std(X, axis = 0) ▣ Image cropping & resizing □ Segmentation model: 64 x 80 px □ Classification model: 224 x 224 px 35
  36. 36. Segmentation model: U-Net architecture 36 ▣ Convolutional Networks for Biomedical Image Segmentation by Olaf Ronneberger et al. Binary Mask
  37. 37. Segmentation model: training parameters 37 ▣ U-Net trained from scratch (small image size) ▣ Weights randomly initialized ▣ Loss function: □ Dice coefficient ▣ Adam optimizer (Stochastic gradient-based optimization): □ Learning rate: 10e-5 ▣ Batch size: 32 ▣ Training epochs: 500 epochs ▣ 13 sec / epoch on NVidia GeForce GTX TITAN X GPU
  38. 38. Objective To verify our hypothesis: 1. Unaltered lesion classification 2. Perfectly segmented lesion classification 3. Automatically segmented lesion classification 38 Logical AND operation Logical AND operation Original Binary Mask (perfect) Binary Mask obtained with the U-Net Previous segmentation of the skin lesion improves the accuracy and sensitivity of a Deep Learning classification model. (1) (2) (3)
  39. 39. Method Scheme (reminder) 39
  40. 40. Classification Model: VGG-16 Architecture 40 ▣ Five Convolutional Blocks (2D conv.) ▣ 3 x 3 receptive field ▣ ReLU as Activation Functions ▣ Max-Pooling ▣ Classifier block: □ 3 FC Layers at the top of the network
  41. 41. Fine-tuning the VGG-16 Architecture 41 ▣ Weights initialized with the VGG-16 pretrained on Imagenet dataset ▣ Freeze bottom of the network ▣ Just train the top of the VGG-16 Train this 41 Freeze these
  42. 42. Classification Model: Loss function ▣ Problem: ISIC dataset classes not balanced □ Validation subset: ■ 304 benign images ■ 75 malignant images ▣ Weighted Loss function: where ρ is defined as 1−frequency appearance (minor class) 42
  43. 43. Classification Model: Training parameters 43 ▣ VGG-16 fine-tuned ▣ Weights initialized with the VGG-16 pretrained on Imagenet dataset ▣ Loss function: □ Weighted Loss function ▣ SGD optimizer (Stochastic gradient-based optimization): □ Learning rate: 10e-5 ▣ Batch size: 32 ▣ Training epochs: 50 epochs ▣ 35 sec / epoch on NVidia GeForce GTX TITAN X GPU
  44. 44. Overfitting ▣ When a model fits the training data too well □ Noise in the training data is learned by the model ▣ How to prevent it? □ Dropout □ Choosing a reduced network (VGG-16 with 138M param. rather than VGG-19 with 144M param.) 44
  45. 45. 4. Experimental Results 45
  46. 46. Segmentation Evaluation 47 Participant Accuracy Dice Coef. Jaccard Index Sensitivity Specificity MIDDLE group 0.9176 0.8689 0.9176 0.9301 0.9544 ▣ Comparing pixel by pixel of each masks: Ground truth Mask obtained JACCARD INDEX:
  47. 47. Segmentation Examples 50 ▣ Satisfactory segmentation examples ▣ Poor segmentation examples
  48. 48. Classification Evaluation 51 Model Accuracy Loss Sensitivity Precision Unaltered lesion clas. 0.8469 0.4723 0.8243 0.9523 Perfectly segmented lesion clas. 0.8390 0.4958 0.8648 0.9621 Automatically segmented lesion clas. 0.8174 0.5144 0.8918 0.9681
  49. 49. Classification Evaluation 52 Model Accuracy Loss Sensitivity Precision Unaltered lesion clas. 0.8469 0.4723 0.8243 0.9523 Perfectly segmented lesion clas. 0.8390 0.4958 0.8648 0.9621 Automatically segmented lesion clas. 0.8174 0.5144 0.8918 0.9681 ▣ With segmentation □ Accuracy decreases □ Loss increases
  50. 50. Classification Evaluation 53 Model Accuracy Loss Sensitivity Precision Unaltered lesion clas. 0.8469 0.4723 0.8243 0.9523 Perfectly segmented lesion clas. 0.8390 0.4958 0.8648 0.9621 Automatically segmented lesion clas. 0.8174 0.5144 0.8918 0.9681 ▣ But...with segmentation □ Sensitivity increases ! □ Precision increases !
  51. 51. Classification Evaluation 54 Model Accuracy Loss Sensitivity Precision Unaltered lesion clas. 0.8469 0.4723 0.8243 0.9523 Perfectly segmented lesion clas. 0.8390 0.4958 0.8648 0.9621 Automatically segmented lesion clas. 0.8174 0.5144 0.8918 0.9681 ▣ But...with segmentation: □ Sensitivity increases ! □ Precision increases ! SENSITIVITY = TP / (TP + FN) PRECISION = TP / (TP + FP)
  52. 52. Sensitivity in Medical Settings ▣ Sensitivity is often considered the most important metric in the medical setting ▣ For early diagnosis □ By missing a False Negatives (true melanoma case) the model would fail in the early diagnosis □ It is better to raise a False Positive than to create a False Negative 55
  53. 53. Classification evaluation 56 Model Accuracy Loss Sensitivity Precision Unaltered lesion clas. 0.8469 0.4723 0.8243 0.9523 Perfectly segmented lesion clas. 0.8390 0.4958 0.8648 0.9621 Automatically segmented lesion clas. 0.8174 0.5144 0.8918 0.9681 ▣ And the Automatically Segmented Model is even BETTER than the Perfectly Segmented □ Physicians can avoid Manual Segmentation tasks
  54. 54. Confusion Matrices 57 False Negatives descending Unaltered Classifier Perfectly Classifier Segmented Classifier
  55. 55. Classification Examples 58
  56. 56. 5. Conclusions 59
  57. 57. Conclusions ▣ DL solution for assisting dermatologists with the diagnosis of skin lesions □ Specifically, for early melanoma detection ▣ Does a previous semantic segmentation improve the performance of a fine-tuned CNN for a 2-class classifier? □ Hypothesis verified ▣ Perfect Segmentation was not needed to obtain the best classification result of the model □ DL Segmentation approach obtained the best sensitivity classification result 60
  58. 58. Conclusions ▣ BioMed 2017 Conference → Paper Accepted □ Title: “Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques” ▣ SIIM 2017 Meeting → Paper Accepted □ Title: “The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images” ▣ MICCAI 2017 Conference → Intention of Paper ▣ MIUA 2017 Conference → Intention of Paper ▣ ISBI 2017 Challenge → Intention of Participation □ Skin Lesion Analysis Towards Melanoma Detection 61
  59. 59. Thanks! Any questions? 62 You can find me at:

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