A systematic comparison of several computational approaches for detecting apoptotic cells in nucleus-stained specimens indicates that classification using image features (deep learning) and numerical features (traditional) achieved the best performance, with ResNet-18 and XGBoost significantly outperforming manually de...
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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