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PRISM-Net AI framework improves breast cancer classification using contralateral breast reference

By PulseAugur Editorial · [2 sources] ·

Researchers have developed PRISM-Net, a novel AI framework designed for breast DCE-MRI classification. This registration-free system leverages contralateral breast features as patient-specific references to learn background-aware representations, improving the discrimination of asymmetric patterns. On the ODELIA dataset, PRISM-Net achieved strong performance metrics, including a Macro AUC of 84.11% on the in-distribution test set and 68.51% on a held-out institution, outperforming existing baseline methods. AI

IMPACT This research could lead to more accurate and patient-specific AI-driven diagnostic tools for breast cancer detection.

RANK_REASON The cluster describes a new AI model presented in a research paper, detailing its methodology and performance metrics on a specific dataset.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

PRISM-Net AI framework improves breast cancer classification using contralateral breast reference

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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    PRISM-Net: Patient-specific reference-guided inter-breast symmetry matching for three-class breast DCE-MRI classification

    Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional lesion-centered diagnosis. Within this broader diagnostic scope, however, patient-specific background variability remains a major sou…

  2. arXiv cs.CV TIER_1 English(EN) · Boya Zhang, Shuaiwen Zhou, Di Kong, Mingxu Wang, Wenbiao Du, Yiman Zhong, Yuexin Duan, Xiawei Yue, Liuquan Cheng, Xiru Li ·

    PRISM-Net: Patient-specific reference-guided inter-breast symmetry matching for three-class breast DCE-MRI classification

    arXiv:2607.26799v1 Announce Type: new Abstract: Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional lesion-centered diagnosis. Within this broader diagnostic scope, however, patient-spe…

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