2017
DOI: 10.1101/197574
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Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning

Abstract: 25Visual analysis of histopathology slides of lung cell tissues is one of the main methods used by

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Cited by 69 publications
(82 citation statements)
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“…65 SETBP1 forms a heterodimer with SET protein and inhibited the tumor suppressive functions of PP2A, SETBP1 mutation is detected in AML and non-small cell lung cancer, which generally blocks its ubiquitination and elicits abnormal high expression. [66][67][68] RHOA is a GTPases that influences multiple biological processes, upregulation of RHOA is associated with tumorigenesis, while instead of intestinal subtype, RHOA mutation is specifically identified in diffuse subtype of GC. 69,70 MUC6 encodes a secretory mucin and protects gastric mucosa, whose reduction inclines chronic mucosal injury and promotes carcinogenesis.…”
Section: Discussionmentioning
confidence: 99%
“…65 SETBP1 forms a heterodimer with SET protein and inhibited the tumor suppressive functions of PP2A, SETBP1 mutation is detected in AML and non-small cell lung cancer, which generally blocks its ubiquitination and elicits abnormal high expression. [66][67][68] RHOA is a GTPases that influences multiple biological processes, upregulation of RHOA is associated with tumorigenesis, while instead of intestinal subtype, RHOA mutation is specifically identified in diffuse subtype of GC. 69,70 MUC6 encodes a secretory mucin and protects gastric mucosa, whose reduction inclines chronic mucosal injury and promotes carcinogenesis.…”
Section: Discussionmentioning
confidence: 99%
“…Deep learning, as the core technology of the rising artificial intelligence (AI) in recent years, has been reported with significantly diagnostic accuracy in medical imaging for automatic detection of lung diseases [13,14,15]. It surpassed human-level performance on the ImageNet image classification task with one million images for training in 2015 [16], showed dermatologist-level performance on classifying skin lesions in 2017 [17] and obtained very impressive results for lung cancer screening in 2019 [13].…”
Section: Introductionmentioning
confidence: 99%
“…Overall, the dataset consisted of 141 WSIs from 56 patients, 28 patients with FCD IIb, and 28 patients with genetically confirmed TSC. H&E stainings were included due to the proven potential of CNNs to extract information not visible to the human observer in H&E slides, 5,22 thus eliminating the need for more complex and expensive immunostainings.…”
Section: Dataset and Region Of Interestmentioning
confidence: 99%
“…Based on these tasks, more abstract functions like disease grading, prognosis prediction, and imaging biomarkers for genetic subtype identification have been established. 4,5 Successful examples include utilization in different types of cancer detection/classification/grading, 6,7 classification of liver cirrhosis, 8 heart failure detection, 9 and classification of Alzheimer plaques. 10 The most commonly used deep learning architectures are convolutional neural networks (CNNs; Figure 1C).…”
Section: Introductionmentioning
confidence: 99%