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Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
April 29, 2024, 4:42 a.m. | Kaichen Xu, Yueyang Ding, Suyang Hou, Weiqiang Zhan, Nisang Chen, Jun Wang, Xiaobo Sun
cs.LG updates on arXiv.org arxiv.org
Abstract: Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportunities for this task. However, current anomaly detection methods struggle to handle domain shifts prevalent in multi-sample and multi-domain single-cell sequencing data, leading to suboptimal performance. Moreover, these methods fall short of distinguishing anomalous cells into pathologically distinct subtypes. In response, we propose ACSleuth, a novel, reconstruction deviation-guided generative framework that integrates the detection, domain …
anomaly anomaly detection arxiv beyond cs.ai cs.lg data detection domain fine-grained q-bio.qm sequencing type
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