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Tech Day Kista Mässa Stockholm 2018

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Zenodia Charpy
Zenodia Charpy

This document discusses using neural networks to build models for classifying pneumonia, performing video semantic segmentation, object detection in video, segmenting organs in CT scans, and learning to play games. It describes getting data, preprocessing, choosing a framework, designing the neural network architecture, training and validating the model, and making predictions in a Jupyter notebook demo. The concepts can apply to other scenarios involving images, video, or other input data.

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1. INTUITION – HOW AI LEARN 2. PUT IT IN CONTEXT – PNEUMONIA 3. PLAN OF ATTACK– HOW TO DO IT 4. DEMO – JUPYTER NOTEBOOK 5. GENERALIZATION
Is this an apple ? apple apple not apple apple apple not appleapple apple not apple ...
apple apple ... not appleapple apple not apple Neural Network Training Validataion Labels Features apple apple not apple 1/3 2/3Performance : (accuracy) 2/3 =67% Prediction apple apple apple
INPUTs : (image + label) Feed to NN NN predicts labels BUILD A MODEL TO CLASSIFY PNEUMONIA The Neural Networks Model Prediction . . Pneumonia Normal Normal Pneumonia ...
All done in one Jupyter notebook 1.Get data 2.Data Process & Explore 3.1 framework (TF, Keras, Torch...etc) 3.2 loss & optimize function 3.Neural Network architecture Deep learning 4.Prediction 3.3 train & validate
DEMO Demo
Tech Day Kista Mässa Stockholm 2018
THE SAME CONCEPT CAN BE APPLIED TO OTHER SCENARIOS
INPUTs : Images of objects of interests Feed to NN Object shapes BUILD A MODEL TO LEARN VIDEO SEMANTIC SEGMENTATION (ENET) Model Prediction: The Neural Networks
INPUTs : Images of objects of interests Feed to NN Object labels + location BUILD A MODEL TO LEARN OBJECT DETECTION IN VIDEO (SSD) Model Prediction: The Neural Networks
Raw input abdominal CT scan Output segment BUILD A MODEL TO SEGMENT 4D CT SCAN ORGANS (NIFTYNET)
BUILD A MODEL TO LEARN TO PLAY ANGRY BIRD
PARKING
Predictedoriginal

More Related Content

Tech Day Kista Mässa Stockholm 2018

  • 1. 1. INTUITION – HOW AI LEARN 2. PUT IT IN CONTEXT – PNEUMONIA 3. PLAN OF ATTACK– HOW TO DO IT 4. DEMO – JUPYTER NOTEBOOK 5. GENERALIZATION
  • 2. Is this an apple ? apple apple not apple apple apple not appleapple apple not apple ...
  • 3. apple apple ... not appleapple apple not apple Neural Network Training Validataion Labels Features apple apple not apple 1/3 2/3Performance : (accuracy) 2/3 =67% Prediction apple apple apple
  • 4. INPUTs : (image + label) Feed to NN NN predicts labels BUILD A MODEL TO CLASSIFY PNEUMONIA The Neural Networks Model Prediction . . Pneumonia Normal Normal Pneumonia ...
  • 5. All done in one Jupyter notebook 1.Get data 2.Data Process & Explore 3.1 framework (TF, Keras, Torch...etc) 3.2 loss & optimize function 3.Neural Network architecture Deep learning 4.Prediction 3.3 train & validate
  • 8. THE SAME CONCEPT CAN BE APPLIED TO OTHER SCENARIOS
  • 9. INPUTs : Images of objects of interests Feed to NN Object shapes BUILD A MODEL TO LEARN VIDEO SEMANTIC SEGMENTATION (ENET) Model Prediction: The Neural Networks
  • 10. INPUTs : Images of objects of interests Feed to NN Object labels + location BUILD A MODEL TO LEARN OBJECT DETECTION IN VIDEO (SSD) Model Prediction: The Neural Networks
  • 11. Raw input abdominal CT scan Output segment BUILD A MODEL TO SEGMENT 4D CT SCAN ORGANS (NIFTYNET)
  • 12. BUILD A MODEL TO LEARN TO PLAY ANGRY BIRD

Editor's Notes

  • #5: https://www.kaggle.com/kmader/electron-microscopy-3d-segmentation/home
  • #6: https://www.kaggle.com/kmader/electron-microscopy-3d-segmentation/home
  • #11: https://www.kaggle.com/kmader/electron-microscopy-3d-segmentation/home
  • #12: https://www.kaggle.com/kmader/electron-microscopy-3d-segmentation/home
  • #13: https://www.kaggle.com/kmader/electron-microscopy-3d-segmentation/home