scholarly journals Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6335 ◽  
Author(s):  
Lilija Aprupe ◽  
Geert Litjens ◽  
Titus J. Brinker ◽  
Jeroen van der Laak ◽  
Niels Grabe
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Immune Cell ◽  
Cancer Tissue ◽  
Cell Detection ◽  
Deep Convolutional Neural Networks ◽  
Quantitative Basis ◽  
Accurate Quantification

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.

scholarly journals 18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks

Radiology ◽  
2020 ◽  
Vol 294 (2) ◽  
pp. 445-452 ◽  
Author(s):  
Ludovic Sibille ◽  
Robert Seifert ◽  
Nemanja Avramovic ◽  
Thomas Vehren ◽  
Bruce Spottiswoode ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Deep Convolutional Neural Networks ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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