Keywords
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Surgical Pathology ClinicsReferences
- Deep learning in cancer diagnosis, prognosis and treatment selection.Genome Med. 2021; 13: 152
- Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA. 2016; 316: 2402-2410
- Artificial intelligence–enabled assessment of the heart rate corrected qt interval using a mobile electrocardiogram device.Circulation. 2021; 143: 1274-1286
- FDA backs clinician-free AI imaging diagnostic tools.Nat Biotechnol. 2018; 36: 673-674
- Artificial intelligence in oncology: current applications and future perspectives.Br J Cancer. 2022; 126: 4-9
- Designing deep learning studies in cancer diagnostics.Nat Rev Cancer. 2021; 21: 199-211
- The Role and Promise of Artificial Intelligence in Medical Toxicology.J Med Toxicol. 2020; 16: 458-464
- Machine Learning in Medicine.N Engl J Med. 2019; 380: 1347-1358
- Deep learning.Nature. 2015; 521: 436-444
- A guide to deep learning in healthcare.Nat Med. 2019; 25: 24-29
- Deep learning in histopathology: the path to the clinic.Nat Med. 2021; 27: 775-784
- Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review.Diagn Pathol. 2021; 16: 24
- Deep learning fast screening approach on cytological whole slides for thyroid cancer diagnosis.Cancers (Basel). 2021; 13: 3891
- Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images.Med Image Anal. 2021; 67: 101814
- Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.Cancer Cytopathology. 2020; 128: 287-295
- Artificial intelligence in cytopathology: A neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears.J Pathol Inform. 2018; 9: 43
- Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study.Ann Transl Med. 2019; 7: 468
- Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN.IEEE Access. 2021; 9: 88429-88438
- Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.PLoS One. 2021; 16: e0257635
- Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features.Mod Pathol. 2021; 34: 862-874
- Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy.J Pathol. 2021; 255: 232-242
- Mapping driver mutations to histopathological subtypes in papillary thyroid carcinoma: applying a deep convolutional neural network.J Clin Med. 2019; 8: 1675
- Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.Nat Cancer. 2020; 1: 800-810
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. ; 2009:248-255. doi:10.1109/CVPR.2009.5206848.
- Cancer of the pancreas - cancer stat facts. SEER.(Available at:) (Accessed April 5, 2022)
- Artificial intelligence and early detection of pancreatic cancer.Pancreas. 2021; 50: 251-279
- Automatic pancreatic ductal adenocarcinoma detection in whole slide images using deep convolutional neural networks.Front Oncol. 2021; 11: 665929
- Graph Convolutional Neural Networks for Histological Classification of Pancreatic Cancer.. 2022; 28 (2022.01.26.22269832)https://doi.org/10.1101/2022.01.26.22269832
Chang YH, Thibault G, Madin O, et al. Deep learning based Nucleus Classification in pancreas histological images. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). ; 2017:672-675. doi:10.1109/EMBC.2017.8036914.
- Deep learning in pancreatic tissue: identification of anatomical structures, pancreatic intraepithelial neoplasia, and ductal adenocarcinoma.Int J Mol Sci. 2021; 22: 5385
- A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy.Sci Rep. 2021; 11: 8454
- Trends in the incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States.JAMA Oncol. 2017; 3: 1335-1342
- Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.J Pathol. 2020; 251: 378-387
- Artificial intelligence and machine learning in the diagnosis and management of gastroenteropancreatic neuroendocrine neoplasms—a scoping review.Diagnostics. 2022; 12: 874
- Comparing deep learning and immunohistochemistry in determining the site of origin for well-differentiated neuroendocrine tumors.J Pathol Inform. 2020; 11: 32
- Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.Sci Rep. 2020; 10: 11064
- Synaptophysin-Ki67 double stain: a novel technique that improves interobserver agreement in the grading of well-differentiated gastrointestinal neuroendocrine tumors.Mod Pathol. 2017; 30: 620-629
- Incidence, demographics, and survival of patients with primary pituitary tumors: a SEER database study in 2004–2016.Sci Rep. 2021; 11: 15155
- Endocrine gland cancer.Cancer. 1995; 75: 338-352
- Machine intelligence in non-invasive endocrine cancer diagnostics.Nat Rev Endocrinol. 2022; 18: 81-95
- Semi-automated validation and quantification of CTLA-4 in 90 different tumor entities using multiple antibodies and artificial intelligence.Lab Invest. 2022; : 1-8https://doi.org/10.1038/s41374-022-00728-4
- The prognostic value of cytotoxic T-lymphocyte antigen 4 in cancers: a systematic review and meta-analysis.Sci Rep. 2017; 7: 42913
- Academics as leaders in the cancer artificial intelligence revolution.Cancer. 2020; 127https://doi.org/10.1002/cncr.33284
- Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.Sci Rep. 2019; 9: 16884
Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW). ; 2018:117-122. doi:10.1109/IIPHDW.2018.8388338.
- Generative Image Translation for Data Augmentation in Colorectal Histopathology Images.Proc Mach Learn Res. 2019; 116: 10-24
- The impact of site-specific digital histology signatures on deep learning model accuracy and bias.Nat Commun. 2021; 12: 4423
- Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.BMJ Open. 2021; 11: e048008