Dead cell detection in fluorescence microscopy is essential for phenotypic screening and drug efficacy assessment. However, automated identification remains challenging due to signal variability, optical artifacts, and colocalization ambiguities across multiple channels. Markers that define nuclear boundaries do not ne...
Prateek Verma, Benjamin Butter, Jian Zhang et al.· 0 citations
This work proposes PURE (Purging Unrelated Representations for Content-Agnostic Forgery Detection), which achieves content-agnostic detection through two complementary components: a Causal Semantic Generative (CSG) mechanism that disentangles semantic representations from forgery-irrelevant nuisance factors, and a Gaus...
Xin-Yu Wu, Dong Li, Minglai Shao et al.· Proceedings of the Thirty-Fi...· 0 citations
Identifying morphologically distinct cell populations in time-lapse fluorescence microscopy is central to understanding how complex tissues develop and remodel, yet remains labor-intensive and subject to observer bias despite advances in cell segmentation. We present a generalizable computational framework for automate...
Prateek Verma, Chloe A. Kuebler, Minh-Hao Van et al.· IEEE/ACM International Confe...· 0 citations
Spatial omics (SO) technologies enable spatially resolved molecular profiling, while hematoxylin and eosin (H&E) imaging remains the gold standard for morphological assessment in clinical pathology. Recent computational advances increasingly place H&E images at the center of SO analysis, bridging morphology with transc...
Ning-Hui Hao, Bo-Shen Yan, Dong Li et al.· Proceedings of the 32nd ACM...· 0 citations
Results show that the MatterSim-D3-based workflow closely reproduces DFT-PBE-D3 optimized structures, while BDIP-Net consistently outperforms existing graph neural network and potential-based approaches for bilayer property prediction.
A message extrapolation mechanism under soft uncertainty constraints is proposed to obtain the diverse counterfactual message distributions and a novel robust representation learning framework for dynamic graph domain generalization, LEMD is proposed.
Xiao-Ran Wei, Chen Zhao, Minglai Shao et al.· Proceedings of the Thirty-Fi...· 0 citations
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