Artificial Intelligence in Digital Pathology Flashcards
What does artificial intelligence aim to teach machines?
Decision making Reasoning Computer Vision Natural language processing Knowledge representation Motion and manipulation
What are two types of AI?
Artificial general intelligence:
Perform across several domains of human intelligence
Turing test
Narrow (weak) AI:
Perform single task as well as humans
Alpha go
What are subsets of AI?
Machine learning:
Algorithms that learn to act from data without being explicitly programmed
Deep learning:
Subset of machine learning based on neural network models
What does machine learning use?
Data to detect patterns
“a set of methods that can automatically detect patterns in data, and then use the . . . patterns to predict future data, or to perform other kinds of decision making. . . ” – Kevin Murphy
How does traditional programme differ to machine learning?
In traditional programming input and program lead to computation and result
In machine learning input and a desired result lead to computation and result
What three categorised is machine learning divided into?
Supervised: Data modelled using label . Used to cluster data
Unsupervised: Data modelled without labels.
Used to cluster data
Reinforcement: Learn from experience. Model uses trial and error to learn from experience. A reward function is used to measure how good each action taken was
Supervised learning techniques include classification and regression
How do both differ?
Classification-
Discrete or categorical labels
Separate observations into categories
Classifier finds mapping between features and classes (categories) that can can predict category for future observations
Logistic regression and random forests common classifiers
Regression- Numerical or continuous output variables Explanatory variables (features) Try to predict a dependent variable Finds function that maps between dependent variable and explanatory variables (features) which can be used to predict a real value for future observations
What does deep learning use?
Neural networks based on biological neurons networks
- Deep learning is based upon what?
2. Why is this powerful?
- Computational networks which are loosely based upon biological neural networks.
- As known as universal function approximates meaning can model complex functions.
What are features of the deep learning model?
Input layer: features for each observation enter the network
Hidden layers: the neurons perform feature extraction and learning task
Output = prediction
In deep learning what is making decisions about its inputs based on the weights it tries to learn during training?
A single neuron
In supervised deep learning a loss function evaluates…
what predictions adjusts the parameters of the network based
What are the two phases within neural networks?
Training and Inference.
During training learning most important features and weights to produce accurate prediction of the weights. Weights extremely important as informing of which features are significant in forming final prediction. This is achieved through an iterative process using two segments: Forward pass and backward pass. Forward pass: Learn which features are important through process using calculation for each neuron to form a decision about its input based upon the weights it tries to learn during training, use features to form a prediction at end of network. Backward pass: Compare prediction to ground truth label using function we defined and adjust weights in the network using an optimisation algorithm - this adjusts the weights proportional to the loss calculated such that can improve on this in next iteration.
Inference: Only single forward pass here whereby weights are static and set at level learnt during training phase. Use this to predict data, extract learnt features and form prediction at end of network
Computer vision is the area teaching machines to understand visual data
What are 4 main tasks often performed using computer vision?
Classification– Correctly label image
Classification and Localization- Correctly label and locate image
Object Detection- Localising all objects of interest in the image
Instance Segmentation- Classification task attempting to apply to all images
In computer vision image date is?
How does this differ to machine learning?
Unstructured and features are learned or engineered from raw pixel values
In machine learning must engineer features manually, whereas here this is not needed
