  Histopathology images are crucial to the study of complex diseases such as
cancer. The histologic characteristics of nuclei play a key role in disease
diagnosis, prognosis and analysis. In this work, we propose a sparse
Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus
detection and feature extraction in histopathology tissue images. Our CAE
detects and encodes nuclei in image patches in tissue images into sparse
feature maps that encode both the location and appearance of nuclei. Our CAE is
the first unsupervised detection network for computer vision applications. The
pretrained nucleus detection and feature extraction modules in our CAE can be
fine-tuned for supervised learning in an end-to-end fashion. We evaluate our
method on four datasets and reduce the errors of state-of-the-art methods up to
42%. We are able to achieve comparable performance with only 5% of the
fully-supervised annotation cost.
