Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides

Abtin Riasatian, Morteza Babaie, Danial Maleki, Shivam Kalra, Mojtaba Valipour, Sobhan Hemati, Manit Zaveri, Amir Safarpoor, Sobhan Shafiei, Mehdi Afshari, Maral Rasoolijaberi, Milad Sikaroudi, Mohd Adnan, Sultaan Shah, Charles Choi, Savvas Damaskinos, Clinton J. V. Campbell, Phedias Diamandis, Liron Pantanowitz, Hany Kashani et al. (2 additional authors not shown)

Abstract: Highlights•Digital Pathology can benefit from computerized search methods.•More discriminative image features result in more accurate search and classification.•Training a deep network by a variety of tumor types will provide better image features.•Using image patches at 20X magnification for training will help network to focus on cell nuclei distributions and shapes.•Fine-tuned and trained DenseNet with 7 million weights to create a new network, KimiaNet, is customized for computational pathology.•KimiaNet can be used as “feature extractor” for image analysis.•An algorithm is proposed to employ unlabeled whole slide images for KimiaNet.•KimiaNet shows excellent results using three public histopathology datasets, among other an average 44% accuracy increase for 12 tumor types.