Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer

• URL: http://arxiv.org/abs/2607.22703v1
• Date: Sun, 19 Jul 2026 10:03:47 GMT
• Title: Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
• Abstract summary: We construct a Prostate Cancer Histopathology Spectrum dataset (PCa-HSD)<n>We formulate a clinically meaningful four-class classification task, addressing the underrepresentation of benign lesions that are easily confounded with prostate cancer in existing datasets.<n>Our proposed method consistently improves accuracy across backbones, achieving the best average accuracy of 0.633 and JointRecall of 0.768 in five-fold cross-validation on a cohort of 344 patients.
• Score: 29.021037203684227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prostate cancer diagnosis with multiparametric MRI (mpMRI) is commonly based on PI-RADS assessment or binary classification, which suffer from subjectivity and fail to capture clinically relevant pathological heterogeneity. To address this limitation, we construct a Prostate Cancer Histopathology Spectrum Dataset (PCa-HSD) and formulate a clinically meaningful four-class classification task, addressing the underrepresentation of benign lesions that are easily confounded with prostate cancer in existing datasets. We propose Language-guided Segmentation-assisted Diagnostic Transformer model (LSDT), which leverages zero-shot segmentation to provide anatomical priors and performs effective multi-modal slice fusion for classification. Our proposed method consistently improves accuracy across backbones, achieving the best average accuracy of 0.633 and JointRecall of 0.768 in five-fold cross-validation on a cohort of 344 patients. These results demonstrate that integrating pathology supervision and anatomical priors significantly enhances fine-grained prostate MRI classification and provides a more clinically relevant paradigm for risk stratification. Code will be made publicly available in a future revision.

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