260 - Identifying anomaly images using convolutional autoencoders

Code generated in the video can be downloaded from here: https://github.com/bnsreenu/python_for_microscopists Detecting anomaly images using AutoEncoders. (Sorting an entire image as either normal or anomaly) Here, we use both the reconstruction error and also the kernel density estimation based on the vectors in the latent space. We will consider the bottleneck layer output from our autoencoder as the latent space. This code uses the malarial data set but it can be easily applied to any application. Data from: https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html