Paper Info
Title
Variational Networks for Joint Image Reconstruction and Classification of Tumor Immune Cell Interactions in Melanoma Tissue Sections.
Abstract
Immunotherapy is currently revolutionizing the treatment of cancer. Detailed analyses of tumor immune cell interaction in the tumor microenvironment will facilitate an accurate prediction of a patient’s clinical response. The automatic and reliable pre-screening of histological tissue sections for tumor infiltrating immune cells (TILs) will support the development of TIL-based predictive biomarkers for checkpoint immunotherapy. In this paper, a learning approach for image classification is presented, which allows various pattern inquires for different types of tissue section images. The underlying trainable reaction diffusion model combines classification and denoising. The model is trained using a stochastic generation of training data. The effectiveness of this approach is demonstrated for immunofluorescent and for Hematoxylin and Eosin (Hu0026E) stained melanoma section images. A particular focus is on the classification of TILs in the proximity to melanoma cells in an experimental melanoma mouse model and in human melanoma. This new learning approach for images of melanoma tissue sections will refine the strategy for the practical clinical application of biomarker research.
Year
DOI
Venue
2018
10.1007/978-3-662-56537-7_86
Bildverarbeitung für die Medizin
Field
DocType
Citations 
Tumor microenvironment,H&E stain,Computer science,Biomarker (medicine),Immune system,Computational biology,Melanoma,Contextual image classification,Cancer,Immunotherapy
Conference
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Alexander Effland195.41
Michael Hölzel200.34
Teresa Klatzer3723.16
Erich Kobler4622.50
Jennifer Landsberg500.34
Leonie Neuhäuser600.34
Thomas Pock73858174.49
Martin Rumpf823018.97