{"slug": "histopathological-spectrum-guided-prostate-stratification-via-segmentation", "title": "Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer", "summary": "Researchers developed the Language-guided Segmentation-assisted Diagnostic Transformer (LSDT) model, which uses zero-shot segmentation and multi-modal slice fusion to classify prostate cancer into four clinically meaningful categories based on histopathology. The model achieved an average accuracy of 0.633 and JointRecall of 0.768 in five-fold cross-validation on a cohort of 344 patients, outperforming existing methods by integrating pathology supervision and anatomical priors.", "body_md": "arXiv:2607.22703v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/histopathological-spectrum-guided-prostate-stratification-via-segmentation", "canonical_source": "https://arxiv.org/abs/2607.22703", "published_at": "2026-07-28 04:00:00+00:00", "updated_at": "2026-07-28 04:08:09.597179+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision"], "entities": ["LSDT", "PCa-HSD", "PI-RADS"], "alternates": {"html": "https://wpnews.pro/news/histopathological-spectrum-guided-prostate-stratification-via-segmentation", "markdown": "https://wpnews.pro/news/histopathological-spectrum-guided-prostate-stratification-via-segmentation.md", "text": "https://wpnews.pro/news/histopathological-spectrum-guided-prostate-stratification-via-segmentation.txt", "jsonld": "https://wpnews.pro/news/histopathological-spectrum-guided-prostate-stratification-via-segmentation.jsonld"}}