Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images
Authors
Keywords
-
Journal
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
Volume 99, Issue -, Pages 102093
Publisher
Elsevier BV
Online
2022-06-17
DOI
10.1016/j.compmedimag.2022.102093
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Data-efficient and weakly supervised computational pathology on whole-slide images
- (2021) Ming Y. Lu et al. Nature Biomedical Engineering
- Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
- (2019) Wouter Bulten et al. Scientific Reports
- Applications of machine learning in drug discovery and development
- (2019) Jessica Vamathevan et al. NATURE REVIEWS DRUG DISCOVERY
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
- (2019) Gabriele Campanella et al. NATURE MEDICINE
- Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
- (2019) Kaustav Bera et al. Nature Reviews Clinical Oncology
- Redundant angiogenic signaling and tumor drug resistance
- (2018) Rajesh N. Gacche et al. DRUG RESISTANCE UPDATES
- A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models
- (2018) Shayan Tabe-Bordbar et al. Scientific Reports
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- Pembrolizumab in patients with programmed death ligand 1–positive advanced ovarian cancer: Analysis of KEYNOTE-028
- (2018) Andrea Varga et al. GYNECOLOGIC ONCOLOGY
- The development and use of vascular targeted therapy in ovarian cancer
- (2017) Dana M. Chase et al. GYNECOLOGIC ONCOLOGY
- Fully Convolutional Networks for Semantic Segmentation
- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Tumor Microvessel Density as a Potential Predictive Marker for Bevacizumab Benefit: GOG-0218 Biomarker Analyses
- (2017) Carlos Bais et al. JNCI-Journal of the National Cancer Institute
- Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer
- (2017) Catherine M Phelan et al. NATURE GENETICS
- Roles of tumor heterogeneity in the development of drug resistance: A call for precision therapy
- (2017) Duojiao Wu et al. SEMINARS IN CANCER BIOLOGY
- Tumor Microvessel Density as a Potential Predictive Marker for Bevacizumab Benefit: GOG-0218 Biomarker Analyses
- (2017) Carlos Bais et al. JNCI-Journal of the National Cancer Institute
- Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
- (2017) Angel Cruz-Roa et al. Scientific Reports
- Anti-angiogenic agents in ovarian cancer: past, present, and future
- (2016) B. J. Monk et al. ANNALS OF ONCOLOGY
- Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images
- (2016) Korsuk Sirinukunwattana et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- State of the science: Emerging therapeutic strategies for targeting angiogenesis in ovarian cancer
- (2015) Whitney Graybill et al. GYNECOLOGIC ONCOLOGY
- GnRH and GnRH receptors in the pathophysiology of the human female reproductive system
- (2015) Roberto Maggi et al. HUMAN REPRODUCTION UPDATE
- Standard chemotherapy with or without bevacizumab for women with newly diagnosed ovarian cancer (ICON7): overall survival results of a phase 3 randomised trial
- (2015) Amit M Oza et al. LANCET ONCOLOGY
- Prognostic importance of cell-free DNA in chemotherapy resistant ovarian cancer treated with bevacizumab
- (2014) Karina Dahl Steffensen et al. EUROPEAN JOURNAL OF CANCER
- Breast Cancer Histopathology Image Analysis: A Review
- (2014) Mitko Veta et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- The challenge of intratumour heterogeneity in precision medicine
- (2014) J. Seoane et al. JOURNAL OF INTERNAL MEDICINE
- Markers of Response for the Antiangiogenic Agent Bevacizumab
- (2013) Diether Lambrechts et al. JOURNAL OF CLINICAL ONCOLOGY
- Latest research and treatment of advanced-stage epithelial ovarian cancer
- (2013) Robert L. Coleman et al. Nature Reviews Clinical Oncology
- Antiangiogenic agents as a maintenance strategy for advanced epithelial ovarian cancer
- (2012) Bradley J. Monk et al. CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY
- Changes in Tumor Blood Flow as Measured by Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) May Predict Activity of Single Agent Bevacizumab in Recurrent Epithelial Ovarian (EOC) and Primary Peritoneal Cancer (PPC) Patients: An exploratory analysis of a Gynecologic Oncology Group Phase II study
- (2012) Dana M. Chase et al. GYNECOLOGIC ONCOLOGY
- OCEANS: A Randomized, Double-Blind, Placebo-Controlled Phase III Trial of Chemotherapy With or Without Bevacizumab in Patients With Platinum-Sensitive Recurrent Epithelial Ovarian, Primary Peritoneal, or Fallopian Tube Cancer
- (2012) Carol Aghajanian et al. JOURNAL OF CLINICAL ONCOLOGY
- Significance of vascular endothelial growth factor in growth and peritoneal dissemination of ovarian cancer
- (2011) Samar Masoumi Moghaddam et al. CANCER AND METASTASIS REVIEWS
- Incorporation of Bevacizumab in the Primary Treatment of Ovarian Cancer
- (2011) Robert A. Burger et al. NEW ENGLAND JOURNAL OF MEDICINE
- Maintenance Treatment with Bevacizumab Prolongs Survival in an In vivo Ovarian Cancer Model
- (2008) S. Mabuchi et al. CLINICAL CANCER RESEARCH
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now