PathoClass-BRCA: Reframing Pathology Report Generation as Guideline-Aligned Multi-Task Classification

Histopathology is the gold standard for cancer diagnosis, and pathology reports directly inform treatment decisions. Although recent deep learning approaches aim to automate report generation from whole-slide images, most methods inherit training objectives and evaluation protocols from image captioning, adopting token-level losses and n-gram metrics that prioritize lexical similarity over diagnostic correctness. To address these limitations, we reformulate pathology report generation as a guideline-aligned multi-task classification problem. Our approach predicts clinically relevant pathological features defined by the International Collaboration on Cancer Reporting guidelines, thereby enabling structured and diagnostically meaningful automated reporting. Our contributions are threefold: i) we introduce PATHOCLASS-BRCA, the first breast cancer benchmark that reframes pathology report generation as guideline-aligned multi-task classification of clinically relevant pathological features; ii) we propose a pathological feature-level semantic evaluation protocol that maps free-text reports to guideline-defined pathological labels, enabling clinically meaningful evaluation of conventional report generation models using classification metrics; and iii) we demonstrate that classification-oriented adaptation of report generation backbones significantly improves the recognition of clinically meaningful histopathological features. The source code and dataset are publicly released at https://github.com/AImageLab-zip/PathoClassBRCA.