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CaresAI at SMM4H-HeaRD 2026: Predicting TNM Staging
arXiv:2607.03466v1 Announce Type: new Abstract: This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026. The problem is framed as three multi-label classification tasks. We explore both classical and deep learning approaches using Term Frequency-Inverse Document Frequency (TF-IDF) features and embeddings from ClinicalBERT, BioBERT, and PubMedBERT. These representatio...
arXiv cs.CL
·Joseph Itopa Abubakar, Jorge Jarme, Favour Igwezeke, Mary Adewunmi
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