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A-Z of AI in Radiology

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A quick overview of some of the common terms and phrases used in artificial intelligence, for radiologists.

Published in: Data & Analytics
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A-Z of AI in Radiology

  1. 1. A-Z of AI in radiology Dr Hugh Harvey
  2. 2. Artificial Intelligence A field of study that combines statistics, computer science and engineering to develop systems capable of performing specific tasks at or above human ability.
  3. 3. Big Data Large structured or unstructured datasets that are so complex that traditional data processing is inadequate to deal with them.
  4. 4. Convolutional Neural Network A class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery
  5. 5. Deep Learning A sub-type of machine learning using neural networks to analyse data through multiple layers of complex processing
  6. 6. Edge Segmentation The separation of an image into anatomical parts or other segments of interest
  7. 7. F1- Score An accuracy measure of binary classification tasks (also known as the DICE coefficient) that doesn’t take true negatives into account
  8. 8. Ground Truth An idealised perfect dataset with robustly labelled and verified data points INPUT DATA + OUTCOME + VERIFICATION = GROUND TRUTH
  9. 9. Hyper- parameter A parameter whose value is manually set before training a machine learning or deep learning model
  10. 10. Image Classification The problem of identifying to which category an image belongs, on the basis of a training set of data containing known observations
  11. 11. Jupyter Open source web-based computational notebook popular with data scientists
  12. 12. K- Nearest Neighbours Non-parametric classification based on the distance in feature space between data points
  13. 13. Labelled Data Metadata that has been assigned to input data
  14. 14. Machine Learning A sub-type of artificial intelligence referring to the ability of computers to learn without being explicitly programmed
  15. 15. Natural Language Processing An area of computer science concerned with the analysis and synthesis of natural human language and speech
  16. 16. Overfitting The production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably
  17. 17. Predictive Analytics A range of statistical techniques to estimate, or ‘predict’, future outcomes
  18. 18. Q- Learning Reinforcement learning of policy, which helps an agent decide what action to take under what circumstances
  19. 19. ROC Curve A plot of test sensitivity as the y coordinate versus its 1-specificity as the x coordinate
  20. 20. Sensitivity & Specificity The true positive and true negative rate of a diagnostic test
  21. 21. Tensor A 3 or more dimensional array of numbers that represents a complex geometric object
  22. 22. Unsupervised Learning The process by which an algorithm is trained on previously unseen input (x) data without labelled outputs (y) to find hidden patterns or groupings
  23. 23. Validation Set An independent dataset not used for training or calibration purposes
  24. 24. Weights Learnable parameters within a network that dictate the strength of an input
  25. 25. X- Entropy The most widely used loss function used to train classification neural networks
  26. 26. Y- Combinator US-based seed accelerator organisation that created a new model for funding early stage startups
  27. 27. Z- Test Determines to what extent a data point is away from the mean of the data set, in standard deviation

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