Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers
Authors
Keywords
-
Journal
GUT
Volume -, Issue -, Pages gutjnl-2020-322880
Publisher
BMJ
Online
2020-11-20
DOI
10.1136/gutjnl-2020-322880
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Reply to the letter to the editor: ‘Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images’
- (2020) Achim Hekler et al. EUROPEAN JOURNAL OF CANCER
- Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
- (2020) Ole-Johan Skrede et al. LANCET
- Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours
- (2020) Osamu Iizuka et al. Scientific Reports
- Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
- (2020) Alessandro Repici et al. GASTROENTEROLOGY
- Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
- (2020) Khalid AbdulJabbar et al. NATURE MEDICINE
- Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning
- (2020) Sidong Liu et al. Scientific Reports
- Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning
- (2020) Amelie Echle et al. GASTROENTEROLOGY
- Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning
- (2020) Korsuk Sirinukunwattana et al. GUT
- Accuracy and Efficiency of Deep-Learning–Based Automation of Dual Stain Cytology in Cervical Cancer Screening
- (2020) Nicolas Wentzensen et al. JNCI-Journal of the National Cancer Institute
- Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group
- (2020) Viknesh Sounderajah et al. NATURE MEDICINE
- Development of AI-based pathology biomarkers in gastrointestinal and liver cancer
- (2020) Jakob N. Kather et al. Nature Reviews Gastroenterology & Hepatology
- Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
- (2020) Christian Marzahl et al. Scientific Reports
- Deep learning‐based classification and mutation prediction from histopathological images of hepatocellular carcinoma
- (2020) Haotian Liao et al. Clinical and Translational Medicine
- Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning
- (2020) Mingyu Chen et al. npj Precision Oncology
- Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides
- (2020) Jason W. Wei et al. JAMA Network Open
- Quantitative Whole Slide Assessment of Tumor-Infiltrating CD8-Positive Lymphocytes in ER-Positive Breast Cancer in Relation to Clinical Outcome
- (2020) Danielle Krijgsman et al. IEEE Journal of Biomedical and Health Informatics
- Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
- (2019) Jakob Nikolas Kather et al. PLOS MEDICINE
- Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
- (2019) Oscar G. F. Geessink et al. CELLULAR ONCOLOGY
- Deep learning for cellular image analysis
- (2019) Erick Moen et al. NATURE METHODS
- Genetic diversity of tumors with mismatch repair deficiency influences anti–PD-1 immunotherapy response
- (2019) Rajarsi Mandal et al. SCIENCE
- Molecular and histological correlations in liver cancer
- (2019) Julien Calderaro et al. JOURNAL OF HEPATOLOGY
- Computational Pathology Definitions, Best Practices, and Recommendations for Regulatory Guidance: A White Paper from the Digital Pathology Association
- (2019) Esther Abels et al. JOURNAL OF PATHOLOGY
- Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
- (2019) Jakob Nikolas Kather et al. NATURE MEDICINE
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
- (2019) Gabriele Campanella et al. NATURE MEDICINE
- Development and Validation of a Pathology Image Analysis-based Predictive Model for Lung Adenocarcinoma Prognosis - A Multi-cohort Study
- (2019) Xin Luo et al. Scientific Reports
- Detection of lung cancer lymph node metastases from whole-slide histopathological images using a two-step deep learning approach
- (2019) Hoa Hoang Ngoc Pham et al. AMERICAN JOURNAL OF PATHOLOGY
- Performance of an artificial intelligence algorithm for reporting urine cytopathology
- (2019) Adit B. Sanghvi et al. CANCER CYTOPATHOLOGY
- Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images
- (2019) Achim Hekler et al. EUROPEAN JOURNAL OF CANCER
- Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
- (2019) Kaustav Bera et al. Nature Reviews Clinical Oncology
- Deep-Learning System Detects Neoplasia in Patients With Barrett’s Esophagus With Higher Accuracy Than Endoscopists in a Multi-Step Training and Validation Study with Benchmarking
- (2019) A.J. de Groof et al. GASTROENTEROLOGY
- Deep learning-based classification of mesothelioma improves prediction of patient outcome
- (2019) Pierre Courtiol et al. NATURE MEDICINE
- A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma
- (2019) Muhammad Shaban et al. Scientific Reports
- Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks
- (2019) Justin Tyler Pontalba et al. Frontiers in Bioengineering and Biotechnology
- Next generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis
- (2019) Anil V. Parwani Diagnostic Pathology
- Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
- (2019) David Tellez et al. MEDICAL IMAGE ANALYSIS
- Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation
- (2019) Davood Karimi et al. IEEE Journal of Biomedical and Health Informatics
- Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study
- (2019) Ohad Kott et al. European Urology Focus
- Macrotrabecular-massive hepatocellular carcinoma: A distinctive histological subtype with clinical relevance
- (2018) Marianne Ziol et al. HEPATOLOGY
- Predicting cancer outcomes from histology and genomics using convolutional networks
- (2018) Pooya Mobadersany et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Genomics and emerging biomarkers for immunotherapy of colorectal cancer
- (2018) Jakob Nikolas Kather et al. SEMINARS IN CANCER BIOLOGY
- Deep learning based tissue analysis predicts outcome in colorectal cancer
- (2018) Dmitrii Bychkov et al. Scientific Reports
- CellProfiler 3.0: Next-generation image processing for biology
- (2018) Claire McQuin et al. PLOS BIOLOGY
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer
- (2018) Korsuk Sirinukunwattana et al. Scientific Reports
- Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
- (2018) David F. Steiner et al. AMERICAN JOURNAL OF SURGICAL PATHOLOGY
- Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours
- (2018) Nina Linder et al. JOURNAL OF CLINICAL PATHOLOGY
- Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
- (2018) John R. Zech et al. PLOS MEDICINE
- A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
- (2018) Mark D. Zarella et al. ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
- (2017) Babak Ehteshami Bejnordi et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification
- (2017) Julien Calderaro et al. JOURNAL OF HEPATOLOGY
- Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
- (2016) Wei Luo et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Multi-class texture analysis in colorectal cancer histology
- (2016) Jakob Nikolas Kather et al. Scientific Reports
- PD-1 Blockade in Tumors with Mismatch-Repair Deficiency
- (2015) Dung T. Le et al. NEW ENGLAND JOURNAL OF MEDICINE
- New Metrics for Assessing Diagnostic Potential of Candidate Biomarkers
- (2012) J. W. Pickering et al. Clinical Journal of the American Society of Nephrology
- Fiji: an open-source platform for biological-image analysis
- (2012) Johannes Schindelin et al. NATURE METHODS
- Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling
- (2012) Y. Yuan et al. Science Translational Medicine
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 MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started