Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
This study proposes a Language-guided Segmentation-assisted Diagnostic Transformer (LSDT) for four-class prostate cancer classification on mpMRI, constructing a Prostate Cancer Histopathology Spectrum Dataset (PCa-HSD) and leveraging zero-shot segmentation for anatomical priors. In five-fold cross-validation on 344 patients, it achieves 0.633 average accuracy and 0.768 JointRecall, significantly improving fine-grained classification.
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[Submitted on 19 Jul 2026]
Title:Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
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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.
Comments: 17 pages, 6 figures, and 4 tables. Code will be made publicly available in a future revision
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.22703 [cs.CV]
(or arXiv:2607.22703v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22703
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Leyang Li [view email] [v1] Sun, 19 Jul 2026 10:03:47 UTC (9,322 KB)
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