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arXiv AI By Yanhao Huang, Shibo Feng, Wanjin Feng, Peilin Zhao, Chunyan Miao

MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis

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MedFlow is a class‑aware multi‑scale flow matching framework designed to synthesize medical time‑series data. It uses a vector‑quantized multi‑scale tokenizer to capture both coarse and fine temporal patterns, and introduces Token Marginal Guidance to steer generation toward minority‑class characteristics. Experiments on four public datasets show MedFlow outperforms diffusion baselines, improving AUPRC by 5.8%, reducing Context‑FID by 88.6%, and achieving 3.8× higher sampling throughput.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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