Deep learning with TensorFlow Probability in cancer prediction with reporting confidence

Description

Deep learning, which involves powerful black box predictors, has achieved state-of-the-art performance in medical imaging analysis, such as segmentation and classification for diagnosis. However, in spite of these successes, these methods focus exclusively on improving accuracy of point predictions without assessing the quality of their outputs. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology.

Biraja Ghoshal walks you through creating probabilistic image classification models using Bayesian deep learning with TensorFlow Probability. Learn how to classify images using deep learning, implement convolutional neural networks, improve the model by batch normalization, dropout, and estimate uncertainty in cancer prediction.