  Feature vectors provided by pre-trained deep artificial neural networks have
become a dominant source for image representation in recent literature. Their
contribution to the performance of image analysis can be improved through
finetuning. As an ultimate solution, one might even train a deep network from
scratch with the domain-relevant images, a highly desirable option which is
generally impeded in pathology by lack of labeled images and the computational
expense. In this study, we propose a new network, namely KimiaNet, that employs
the topology of the DenseNet with four dense blocks, fine-tuned and trained
with histopathology images in different configurations. We used more than
240,000 image patches with 1000x1000 pixels acquired at 20x magnification
through our proposed "highcellularity mosaic" approach to enable the usage of
weak labels of 7,126 whole slide images of formalin-fixed paraffin-embedded
human pathology samples publicly available through the The Cancer Genome Atlas
(TCGA) repository. We tested KimiaNet using three public datasets, namely TCGA,
endometrial cancer images, and colorectal cancer images by evaluating the
performance of search and classification when corresponding features of
different networks are used for image representation. As well, we designed and
trained multiple convolutional batch-normalized ReLU (CBR) networks. The
results show that KimiaNet provides superior results compared to the original
DenseNet and smaller CBR networks when used as feature extractor to represent
histopathology images.
