Morph-ISR is proposed, a morphology-aware implicit super-resolution framework for DP that restores diagnostically relevant details with sub-pixel precision and enhances structural fidelity, demonstrating superior preservation of diagnostically relevant cellular boundaries and nuclear textures.
Jia-Ming Liang, Qihui Han, Hao-Lin Chen et al.· 0 citations
Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert out-performs state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting.
Xiao-Qi Sheng, Jia-Wen Liu, Yu-Tong Li et al.· 0 citations
Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert outperforms state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting regulatory mechanisms.
Xiao-Qi Sheng, Jia-Wen Liu, Yu-Tong Li et al.· Proceedings of the Thirty-Fi...· 0 citations
Experimental results show that DeMixPert effectively captures heterogeneous single-cell perturbation responses and achieves superior performance across unseen-perturbation settings.
Jia-Wen Liu, Xu Cao, Yu-Tong Li et al.· 0 citations
This work investigates MRI-based Microbial Density Stratification as a patient-level representation learning task, and Center Heatmap-driven Macro-micro modeling Network (CHM-Net) is introduced for this task, establishing the link between imaging phenotypes and microbial states through center heatmap-guided small-lesio...
Jiaming Liang, Hao Chen, Ting Li et al.· 0 citations
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