ArticleBMC bioinformatics2021
Deep active learning for classifying cancer pathology reports.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Development and Validation of a Large Language Model Case Identification Strategy for Eosinophilic Esophagitis.Gastro hep advances · 2026Article
- Comparison of active learning algorithms in classifying head computed tomography reports using bidirectional encoder representations from transformers.International journal of computer assisted radiology and surgery · 2025Article
- Detection and classification of venous thromboembolism through image test reports analysis using active learning and deep learning.PloS one · 2025Article
- The SEER Program's evolution: supporting clinically meaningful population-level research.Journal of the National Cancer Institute. Monographs · 2024Article
- Article
- Deep learning uncertainty quantification for clinical text classification.Journal of biomedical informatics · 2024Article
- An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images.Insights into imaging · 2023Article
- Active Learning Optimisation of Binary Coded Metasurface Consisting of Wideband Meta-Atoms.Sensors (Basel, Switzerland) · 2023Article
- A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction.PloS one · 2023Article
- Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.Biomolecules · 2022Article
- Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review.Medicina (Kaunas, Lithuania) · 2022Review
- Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports.JAMIA open · 2022Article
- A Keyword-Enhanced Approach to Handle Class Imbalance in Clinical Text Classification.IEEE journal of biomedical and health informatics · 2022Article
- Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.Frontiers in digital health · 2022Article
- Current and Emerging Informatics Initiatives Impactful to Cancer Registries.Journal of registry management · 2022Review
- Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types.Journal of biomedical informatics · 2022Article
- Labels in a haystack: Approaches beyond supervised learning in biomedical applications.Patterns (New York, N.Y.) · 2021Review
- Assigning ICD-O-3 Codes to Pathology Reports using Neural Multi-Task Training with Hierarchical Regularization.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundAutomated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model.
resultsWe compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes.
conclusionsActive learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.