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Qatar University

Academic institutionasia · qa
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Research library61linked papers
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Selected work

Representative Papers

BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization

Aug 10, 2026

This study addresses the severe domain shift in mammography caused by equipment from different vendors, which significantly hinders the cross-site generalization of AI models. To tackle this issue, the authors introduce two new datasets, BreastMammo and DenseMammo, and propose a foreground-specific histogram matching protocol tailored for mammographic images. Integrated with a Swin Transformer backbone, this approach establishes the first domain generalization benchmark for breast density classification. Evaluated via five-fold cross-validation and external testing on datasets such as TNMammo and LUMINA, the method achieves an internal AUC of 98.32%, substantially outperforming existing techniques like MixStyle and discrete Fourier transform–based methods. The results demonstrate its effectiveness in mitigating domain shifts arising from clinical source variations and enhancing model robustness across domains.

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Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

Aug 07, 2026

This work addresses the limitations of existing deep random vector functional-link networks (dRVFL), which treat all samples equally and thus struggle with real-world data contaminated by noise and outliers, leading to degraded discriminative performance due to error propagation through hidden layers. To overcome this, the study introduces an intuitionistic fuzzy mechanism into dRVFL for the first time, adaptively computing membership and non-membership degrees by jointly modeling each sample’s distance to its class center and the heterogeneity of its local neighborhood. This enables effective differentiation among clean, noisy, and anomalous samples, assigning them differentiated weights accordingly. Combined with kernel-space neighborhood analysis and ensemble learning, the proposed method significantly outperforms current state-of-the-art fuzzy and non-fuzzy approaches on UCI and KEEL benchmark datasets under both Gaussian-noise and noise-free settings, demonstrating superior robustness and generalization capability.

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Recent publications

Latest Papers

BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization

Aug 10, 2026

This study addresses the severe domain shift in mammography caused by equipment from different vendors, which significantly hinders the cross-site generalization of AI models. To tackle this issue, the authors introduce two new datasets, BreastMammo and DenseMammo, and propose a foreground-specific histogram matching protocol tailored for mammographic images. Integrated with a Swin Transformer backbone, this approach establishes the first domain generalization benchmark for breast density classification. Evaluated via five-fold cross-validation and external testing on datasets such as TNMammo and LUMINA, the method achieves an internal AUC of 98.32%, substantially outperforming existing techniques like MixStyle and discrete Fourier transform–based methods. The results demonstrate its effectiveness in mitigating domain shifts arising from clinical source variations and enhancing model robustness across domains.

0 citationsRead paper

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

Aug 07, 2026

This work addresses the limitations of existing deep random vector functional-link networks (dRVFL), which treat all samples equally and thus struggle with real-world data contaminated by noise and outliers, leading to degraded discriminative performance due to error propagation through hidden layers. To overcome this, the study introduces an intuitionistic fuzzy mechanism into dRVFL for the first time, adaptively computing membership and non-membership degrees by jointly modeling each sample’s distance to its class center and the heterogeneity of its local neighborhood. This enables effective differentiation among clean, noisy, and anomalous samples, assigning them differentiated weights accordingly. Combined with kernel-space neighborhood analysis and ensemble learning, the proposed method significantly outperforms current state-of-the-art fuzzy and non-fuzzy approaches on UCI and KEEL benchmark datasets under both Gaussian-noise and noise-free settings, demonstrating superior robustness and generalization capability.

0 citationsRead paper