Deep learning detects genetic alterations in cancer histology generated by adversarial networks
暂无分享,去创建一个
Philip Quirke | Jakob Nikolas Kather | Peter Boor | Michael Jendrusch | Titus J Brinker | Christian Trautwein | Amelie Echle | Tom Luedde | Matthias Kloor | Piet A van den Brandt | Jeremias Krause | Heike I Grabsch | Roman David Buelow | Alexander T Pearson | Josien Jenniskens | Kelly Offermans | M. Kloor | T. Brinker | T. Luedde | P. A. van den Brandt | A. Pearson | Jeremias Krause | P. Boor | H. Grabsch | C. Trautwein | A. Echle | P. Quirke | R. D. Bülow | Josien C A Jenniskens | K. Offermans | Michael Jendrusch | J. Jenniskens
[1] N. Coudray,et al. Deep learning links histology, molecular signatures and prognosis in cancer , 2020, Nature Cancer.
[2] Thomas J. Fuchs,et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.
[3] Jakob Nikolas Kather,et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.
[4] Volkmar Schulz,et al. Breaking medical data sharing boundaries by using synthesized radiographs , 2020, Science Advances.
[5] Nasir Rajpoot,et al. SAFRON: Stitching Across the Frontier for Generating Colorectal Cancer Histology Images , 2020, ArXiv.
[6] Alexander T. Pearson,et al. Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. , 2020, Gastroenterology.
[7] Jakob Nikolas Kather,et al. Deep learning in cancer pathology: a new generation of clinical biomarkers , 2020, British Journal of Cancer.
[8] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[9] Steven J. M. Jones,et al. Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.
[10] Molecular testing strategies for Lynch syndrome in people with colorectal cancer , 2022 .
[11] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[12] R A Goldbohm,et al. A large-scale prospective cohort study on diet and cancer in The Netherlands. , 1990, Journal of clinical epidemiology.
[13] Shivam Kalra,et al. Generative models in pathology: synthesis of diagnostic quality pathology images† , 2020, The Journal of pathology.
[14] Jakob Nikolas Kather,et al. Pan-cancer image-based detection of clinically actionable genetic alterations , 2019, Nature Cancer.
[15] Alexander W. Jung,et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis , 2019, Nature Cancer.
[16] Pierre Courtiol,et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images , 2020, Nature Communications.
[17] N. Razavian,et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.
[18] A. Jemal,et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.
[19] Jakob Nikolas Kather,et al. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer , 2020, Nature Reviews Gastroenterology & Hepatology.
[20] J. S. Marron,et al. A method for normalizing histology slides for quantitative analysis , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[21] Geert J. S. Litjens,et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology , 2019, Medical Image Anal..
[22] Jakob Nikolas Kather,et al. Genomics and emerging biomarkers for immunotherapy of colorectal cancer. , 2018, Seminars in cancer biology.
[23] John E. Tomaszewski,et al. Generative modeling for renal microanatomy , 2020, Medical Imaging: Digital Pathology.
[24] E. Martinelli,et al. Hereditary gastrointestinal cancers: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2019, Annals of oncology : official journal of the European Society for Medical Oncology.
[25] Yiping Wang,et al. Synthesis of diagnostic quality cancer pathology images by generative adversarial networks , 2020, The Journal of pathology.
[26] M. Casparie,et al. Pathology Databanking and Biobanking in The Netherlands, a Central Role for PALGA, the Nationwide Histopathology and Cytopathology Data Network and Archive , 2007, Cellular oncology : the official journal of the International Society for Cellular Oncology.
[27] Michael Gadermayr,et al. Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology , 2019, IEEE Transactions on Medical Imaging.
[28] I. Frayling,et al. Molecular testing for Lynch syndrome in people with colorectal cancer: systematic reviews and economic evaluation. , 2017, Health technology assessment.
[29] Jakob Nikolas Kather,et al. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers , 2020, Gut.