arXiv subjects

Gregory Arbour

Publications and source records attributed to Gregory Arbour.

4 recordsLinked to original sources

arXiv2026

Learning from Lost Provenance: Multiple Instance Learning for Cancer Registry Tumor Group Classification

Modernizing cancer registries with deep learning is opening new opportunities to automate labor-intensive tasks such as the coding of pathology reports. However, progress is constrained by the scarcity of report-level human-annotated training data. Cancer registries generate substantial volumes of expert-assigned labels as a routine product of their operations, but these exist at the patient level and are not linked to the individual pathology reports that informed them, limiting their direct use for training models. We develop an efficient framework for training deep learning classifiers by leveraging these operationally-generated labels without requiring per-report human annotation, demonstrated for tumor group classification at the BC Cancer Registry. We use Attention-Based Multiple Instance Learning (ABMIL) to recover the lost link between patient-level labels and the reports that informed them, leveraging the attention the model places on each report to distil a large, noisily-labeled corpus into a compact, high-quality per-report training dataset. A classifier fine-tuned on a distilled dataset achieved a macro F1 of 0.83, outperforming established baselines across most tumor groups. By turning routine operational labels into high-quality training data without additional annotation or large-scale computing infrastructure, ABMIL offers a practical and accessible route to automating cancer registry workflows.

cs.CL↗

arXiv2025

Bridging AI Innovation and Healthcare Needs: Lessons Learned from Incorporating Modern NLP at The BC Cancer Registry

Automating data extraction from clinical documents offers significant potential to improve efficiency in healthcare settings, yet deploying Natural Language Processing (NLP) solutions presents practical challenges. Drawing upon our experience implementing various NLP models for information extraction and classification tasks at the British Columbia Cancer Registry (BCCR), this paper shares key lessons learned throughout the project lifecycle. We emphasize the critical importance of defining problems based on clear business objectives rather than solely technical accuracy, adopting an iterative approach to development, and fostering deep interdisciplinary collaboration and co-design involving domain experts, end-users, and ML specialists from inception. Further insights highlight the need for pragmatic model selection (including hybrid approaches and simpler methods where appropriate), rigorous attention to data quality (representativeness, drift, annotation), robust error mitigation strategies involving human-in-the-loop validation and ongoing audits, and building organizational AI literacy. These practical considerations, generalizable beyond cancer registries, provide guidance for healthcare organizations seeking to successfully implement AI/NLP solutions to enhance data management processes and ultimately improve patient care and public health outcomes.

cs.CL↗

arXiv2025

Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare

This study aims to guide language model selection by investigating: 1) the necessity of finetuning versus zero-shot usage, 2) the benefits of domain-adjacent versus generic pretrained models, 3) the value of further domain-specific pretraining, and 4) the continued relevance of Small Language Models (SLMs) compared to Large Language Models (LLMs) for specific tasks. Using electronic pathology reports from the British Columbia Cancer Registry (BCCR), three classification scenarios with varying difficulty and data size are evaluated. Models include various SLMs and an LLM. SLMs are evaluated both zero-shot and finetuned; the LLM is evaluated zero-shot only. Finetuning significantly improved SLM performance across all scenarios compared to their zero-shot results. The zero-shot LLM outperformed zero-shot SLMs but was consistently outperformed by finetuned SLMs. Domain-adjacent SLMs generally performed better than the generic SLM after finetuning, especially on harder tasks. Further domain-specific pretraining yielded modest gains on easier tasks but significant improvements on the complex, data-scarce task. The results highlight the critical role of finetuning for SLMs in specialized domains, enabling them to surpass zero-shot LLM performance on targeted classification tasks. Pretraining on domain-adjacent or domain-specific data provides further advantages, particularly for complex problems or limited finetuning data. While LLMs offer strong zero-shot capabilities, their performance on these specific tasks did not match that of appropriately finetuned SLMs. In the era of LLMs, SLMs remain relevant and effective, offering a potentially superior performance-resource trade-off compared to LLMs.

cs.CL↗

arXiv2025

ELM: A Hybrid Ensemble of Language Models for Automated Tumor Group Classification in Population-Based Cancer Registries

Background: Population-based cancer registries (PBCRs) manually extract data from unstructured pathology reports, a labor-intensive process where assigning reports to tumor groups can consume 900 person-hours annually for approximately 100,000 reports at a medium-sized registry. Current automated rule-based systems fail to handle the linguistic complexity of this classification task. Materials and Methods: We present ELM (Ensemble of Language Models), a novel hybrid approach combining small, encoder only language models and large language models (LLMs). ELM employs an ensemble of six fine-tuned encoder only models: three analyzing the top portion and three analyzing the bottom portion of each report to maximize text coverage given token limits. A tumor group is assigned when at least five of six models agree; otherwise, an LLM arbitrates using a carefully curated prompt constrained to likely tumor groups. Results: On a held-out test set of 2,058 pathology reports spanning 19 tumor groups, ELM achieves weighted precision and recall of 0.94, representing a statistically significant improvement (p<0.001) over encoder-only ensembles (0.91 F1-score) and substantially outperforming rule-based approaches. ELM demonstrates particular gains for challenging categories including leukemia (F1: 0.76 to 0.88), lymphoma (0.76 to 0.89), and skin cancer (0.44 to 0.58). Discussion: Deployed in production at British Columbia Cancer Registry, ELM has reduced manual review requirements by approximately 60-70%, saving an estimated 900 person-hours annually while maintaining data quality standards. Conclusion: ELM represents the first successful deployment of a hybrid small, encoder only models-LLM architecture for tumor group classification in a real-world PBCR setting, demonstrating how strategic combination of language models can achieve both high accuracy and operational efficiency.

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