machine learning for bone scans(to serve as a proof of concept) our algorithm a convolutional neural...
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Machine Learning for Bone Scans
Benjamin FangDepartment of RadiologyQueen Mary Hospital
Preamble
• Demand for medical imaging ever increasing• Widening service gap• Not enough radiologists• Need new tools to help => AI
Russakovsky et al., 2015
Error rate (lower is better)
Computer surpassed human
Image source: https://recruitingdaily.com/recruiting‐grudge‐match‐wins‐humans‐vs‐machines/
ObjectiveDevelop a computer algorithm that can recognize bone metastasis in a bone scan image.(To serve as a proof of concept)
Our AlgorithmA Convolutional Neural Network
Input layer:512x512x1
10x10x1x32Convolution RELU 4x4 Maxpool
10x10x32x64Convolution RELU 4x4 Maxpool
8x8x64x64Convolution RELU 2x2 Maxpool
8x8x64x64Convolution RELU 2x2 Maxpool 200‐neuron
Fully connected layer
2‐neuronFully connected layer Softmax
4 Convolutional layers 2 Fully connected layers
Total number of learnable parameters: 3200+204800+524288+51200+400+32+64+64+64+200+2=854,586
Output:Probability of Metastasis (treated as positive if >50%)
Convolutional Neural Network (CNN)
• What is it?– A type of artificial neural network– Uses convolutions (a process whereby featuresare extracted from an image)
Artificial neural network
RELU activation function
http://cs231n.stanford.edu/
Neurons in layers
Image source: MathWorkImage source: https://theconversation.com/deep‐learning‐and‐neural‐networks‐77259
Training our algorithm
1. Forward pass: Feed the network a batch of bone scan images and calculate the predictions
2. Loss: Quantify how good the predictions are3. Back propagation: Calculate how each parameter of the network affects the
predictions4. Optimization: Change each parameter a little to improve the predictions5. Iterate: back to step 1
Our dataset
• 106 Bone scan images58: Metastasis present48: No metastasis
• Diagnosis (ground truth) decided by a Nuclear Medicine Specialist with 29 Years experience in bone scan interpretation
Our dataset
1024x1024 pixelsSingle channel (8‐bit grey scale)
No patient demographic infoNo medical history
Anterior whole body scan Posterior whole body scan
Label: ‐Metastasis present or absent
Image Augmentation(Create infinite variations of our images)
Rotation (‐10 to +10 degrees)Translation (‐45 to +45 pixels)Zooming (‐40 to +40 pixels)Occlusion (left, right or none)
Division of data for training/validation/testing
1/3 (35) assigned for testing2/3 (71) assigned for training/validation
Subdivided into 3 parts
Validation
Training Testing
3‐fold cross validation
Final training
106 Bone scans
Training / Validation
Accuracy
Training step
‐ Training ‐‐ Validation ‐
Final test resultsAccuracy: correct/(correct + incorrect) = 33/(33 +2) = 94%Sensitivity: TP /(TP + FN) = 19 / (19 +1) = 95%Specificity: TN /(TN + FP) = 14 / (14 + 1) = 93%AUC (ROC) = 0.94
What did it get wrong?
Conclusion
• CNNs are very powerful• Our bone scan CNN performed very well despite trained on only a small number of images.
What if our training dataset was much larger?Human expert level performance or better probably achievable.
Some other areas where CNN can be applied
Plain radiographCTMRIUSGMicroscopy images: Pathology / microbiology Clinical photos: dermatologyCapsule endoscopy………etc.
Areas involving visual recognition
Thank you
How to utilize these systems
• 2nd read• Screen unreported exams• Exam prioritization
How to build more of these AI algorithms to help us
• Current limiting factor: Lack of well curated, large training datasets
• Way forward:• For future reports: Structured reporting (standard
templates/checklists etc.)• For past reports:
• Data mining algorithms (neural networks again)• Manual data mining unlikely to be feasible.
Loss function
A quantification of how accurate/inaccurate the prediction is.
Cross Entropy:L=−y log(y^)
L : Lossy^ : predictedY : ground truth
Convolution
Image source: http://machinelearninguru.com/computer_vision/basics/convolution/convolution_layer.html
Each value within a kernel is a learnable parameter
3‐fold Training‐Validation
Accuracy
Training steps
Loss decreases with training
Epoch = number of cycles the network has gone through the whole dataset
The Digital Image
Image source: https://developer.apple.com/library/content/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html
What about convolution?
‐ A mathematical method to extract features from an image.
‐ Features are represented in different levels of abstraction
Division of data for training/validation/testing
1/3 (35) assigned for testing2/3 (71) assigned for training/validation
Subdivided into 3 parts
Validation
Training Testing
3‐fold cross validation
Final training
106 Bone scans
Russakovsky et al., 2015
Error rate (lower is better)
CNN propelled computer vision to surpass human
Advent of CCNAll CCNs