ArticleJournal of biomedical informatics2022
Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Classification of Clinical Outcomes in Hospitalized Asian Elephants Using Machine Learning and Survival Analysis: A Retrospective Study (2019-2024).Veterinary sciences · 2025Article
- EnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information.Interdisciplinary sciences, computational life sciences · 2025Article
- Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data.ACS omega · 2025Article
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.Journal of cancer research and clinical oncology · 2024Article
- The SEER Program's evolution: supporting clinically meaningful population-level research.Journal of the National Cancer Institute. Monographs · 2024Article
- TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models.Patterns (New York, N.Y.) · 2024Article
- Time Matters: Examine Temporal Effects on Biomedical Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Topological Superconductors from a Materials Perspective.Chemistry of materials : a publication of the American Chemical Society · 2023Review
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis.Journal of combinatorial optimization · 2023Article
- Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports.JAMIA open · 2022Article
- Current and Emerging Informatics Initiatives Impactful to Cancer Registries.Journal of registry management · 2022Review
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Authors and funding
14 authors.
Funding
Abstract
In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.
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Registered trials
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.