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cs.CL2026
Learning from Lost Provenance: Multiple Instance Learning for Cancer Registry Tumor Group Classification
Leonard Ruocco, Jonathan Simkin, Lovedeep Gondara +2
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 constr…
cs.CL2026
ELM: A Hybrid Ensemble of Language Models for Automated Tumor Group Classification in Population-Based Cancer Registries
Lovedeep Gondara, Jonathan Simkin, Shebnum Devji +2
Background: Population-based cancer registries (PBCRs) manually extract data from unstructured pathology reports, a labor-intensive process where assigning reports to tumor groups…
cs.CL2026
Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registries
Jonathan Simkin, Lovedeep Gondara, Zeeshan Rizvi +5
Population-based cancer registries depend on pathology reports as their primary diagnostic source, yet manual abstraction is resource-intensive and contributes to delays in cancer…