Abstract:
:Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.
journal_name
PeerJjournal_title
PeerJauthors
Aprupe L,Litjens G,Brinker TJ,van der Laak J,Grabe Ndoi
10.7717/peerj.6335subject
Has Abstractpub_date
2019-04-10 00:00:00pages
e6335issn
2167-8359pii
6335journal_volume
7pub_type
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