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Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks.

, , , , , and . INNSBDDL, page 89-98. Springer, (2019)

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Hierarchical attention networks for information extraction from cancer pathology reports., , , , , , , and . JAMIA, 25 (3): 321-330 (2018)Classifying cancer pathology reports with hierarchical self-attention networks., , , , , , , , , and 3 other author(s). Artificial Intelligence in Medicine, (2019)CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules., , , , , , and . BMC Bioinformatics, 19-S (18): 3-8 (2018)Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks., , , , , and . INNSBDDL, page 89-98. Springer, (2019)Semi-Supervised Information Extraction for Cancer Pathology Reports., , , , , , , and . BHI, page 1-4. IEEE, (2019)Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports., , , , , , , , , and 3 other author(s). BHI, page 1-4. IEEE, (2019)Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports., , , and . IEEE J. Biomedical and Health Informatics, 22 (1): 244-251 (2018)Scalable deep text comprehension for Cancer surveillance on high-performance computing., , , , , , , , and . BMC Bioinformatics, 19-S (18): 99-110 (2018)Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension., , , , and . BHI, page 345-348. IEEE, (2018)