Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
出版年份 2021 全文链接
标题
Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
作者
关键词
-
出版物
npj Precision Oncology
Volume 5, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-09-23
DOI
10.1038/s41698-021-00225-9
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images
- (2021) Pooya Mobadersany et al. LABORATORY INVESTIGATION
- Whole slide images reflect DNA methylation patterns of human tumors
- (2020) Hong Zheng et al. npj Genomic Medicine
- A deep learning model to predict RNA-Seq expression of tumours from whole slide images
- (2020) Benoît Schmauch et al. Nature Communications
- Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
- (2020) Jiawen Yao et al. MEDICAL IMAGE ANALYSIS
- Deep learning with multimodal representation for pancancer prognosis prediction
- (2019) Anika Cheerla et al. BIOINFORMATICS
- Local Treatment of Breast Cancer Liver Metastasis
- (2019) Reto Bale et al. Cancers
- Deep learning-based classification of mesothelioma improves prediction of patient outcome
- (2019) Pierre Courtiol et al. NATURE MEDICINE
- Oncogenic Signaling Pathways in The Cancer Genome Atlas
- (2018) Francisco Sanchez-Vega et al. CELL
- Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer
- (2018) Katherine A. Hoadley et al. CELL
- Current Status of Fibroblast Growth Factor Receptor-Targeted Therapies in Breast Cancer
- (2018) Navid Sobhani et al. Cells
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- Expression of Notch Gene and Its Impact on Survival of Patients with Resectable Non-small Cell Lung Cancer
- (2017) Chung-Yu Chen et al. Journal of Cancer
- TGF-β signaling in liver and gastrointestinal cancers
- (2016) L.H. Katz et al. CANCER LETTERS
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets
- (2015) Kornelius Schulze et al. NATURE GENETICS
- Genomic portrait of resectable hepatocellular carcinomas: Implications ofRB1andFGF19aberrations for patient stratification
- (2014) Sung-Min Ahn et al. HEPATOLOGY
- Functional roles of fibroblast growth factor receptors (FGFRs) signaling in human cancers
- (2013) Kai Hung Tiong et al. APOPTOSIS
- Targeting FGFR with Dovitinib (TKI258): Preclinical and Clinical Data in Breast Cancer
- (2013) F. Andre et al. CLINICAL CANCER RESEARCH
- Fibroblast growth factor receptors in breast cancer: expression, downstream effects, and possible drug targets
- (2012) M Tenhagen et al. ENDOCRINE-RELATED CANCER
- FGFR1 Amplification in Squamous Cell Carcinoma of The Lung
- (2012) Rebecca S. Heist et al. Journal of Thoracic Oncology
- 18F-Fluorodeoxy-glucose Positron Emission Tomography Marks MYC-Overexpressing Human Basal-Like Breast Cancers
- (2011) N. Palaskas et al. CANCER RESEARCH
- TP53 Mutations in Nonsmall Cell Lung Cancer
- (2011) Akira Mogi et al. JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY
- Randomized Study of Lapatinib Alone or in Combination With Trastuzumab in Women With ErbB2-Positive, Trastuzumab-Refractory Metastatic Breast Cancer
- (2010) Kimberly L. Blackwell et al. JOURNAL OF CLINICAL ONCOLOGY
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