Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

arXiv:2609.27208 · cs.CV, cs.LG, q-bio.OT · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.LG, q-bio.OT

Submitted: 2026-09-23

Updated: 2026-09-23

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 10× in this paper · explained in 2 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Active Learning — 7× in this paper · explained in 6 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Foundation Models — 2× in this paper · explained in 7 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Monte Carlo dropout — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes

Sources

• ASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics
• MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics
• DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction
• SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning

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