clementsan / cnn-image-classification-uncertainty

AI-based image classification tasks with uncertainty estimation

AI-based image classification tasks with uncertainty

Image classification tasks via CNN models, leveraging Monte Carlo dropout during inference to assess model uncertainty

Related paper: [Gal et al., Dropout as a bayesian estimation: representing model uncertainty in deep learning, ICML 2016

Analysis tasks

Image preprocessing

• Generate small image tiles from larger acquisitions
• Split dataset into training, validation and test sets, using stratification strategy at acquisition level

Image analysis

• Neural network training
• Neural network inference (direct)
• Neural network inference with gradCAM visualization
• Neural network inference with uncertainty assessment

Notes

Neural networks

Use of ResNet models

Deep learning library

Use of fastai_v1 (2019)