构建首个相位差显微镜下细菌多物种识别的开放世界基准，测试模型对未见混合物的泛化能力。
PHOEBI: An Open-World Benchmark for Bacterial Identification in Phase-Contrast Microscopy
- 设计新基准PHOEBI，含12万张图像与40种细菌组合，模拟真实复杂样本。
- 模型在未见组合上F1下降0.39至0.57，暴露聚合器的开放世界失效问题。
- 提出轻量锚点解码器，在未见组合上表现优于训练集，适合临床微生物诊断。
光学显微镜可实现活体细菌的快速、无标记成像，是临床、环境及工业微生物学中物种鉴定的标准工具。然而，实际样本常为多菌混合，且可能包含训练阶段未出现的物种，目前尚无计算机视觉基准测试此类相位差显微镜（PCM）图像中的多标签物种识别。本文提出相位差光学细菌识别基准（PHOEBI），一个湿实验制备的数据集，包含12万张PCM图像，覆盖6种杆状菌的40种组合，并采用留组合外（LCO）评估协议，将完整物种组合留出以模拟模型在已知混合物上训练却需泛化至未知混合物的实际场景。在LCO评估下，所有测试的梯度训练图像聚合器在分布外数据上的F1值均下降0.39至0.57，表明聚合器存在系统性开放世界识别失败，而非视觉表示问题。对十三种不同编码器的线性探测显示，通用与生物医学预训练目标间仅相差约6个百分点的F1，证实表示质量可靠。我们提出三种轻量级锚点解码器，基于共享冻结的块特征池几何建模每种物种存在性，在留出组合上的得分高于分布内验证。
原文摘要 · Abstract (English)
Optical microscopy enables rapid, label-free imaging of live bacteria and is the standard instrument for species identification across clinical, environmental, and industrial microbiology. Yet field samples are routinely polymicrobial and may contain organisms that were never seen during system training, and no computer-vision benchmark tests multi-label species identification from phase-contrast microscopy (PCM) of such mixtures. We introduce Phase-contrast Optical bEnchmark for Bacterial Identification ($\textbf{PHOEBI}$), a wet-lab-prepared dataset of $120{,}000$ PCM images covering $40$ combinations of six rod-shaped species, paired with a leave-combinations-out (LCO) evaluation protocol that holds out entire species combinations to mirror the practical scenario of a model trained on catalogued mixtures that must generalise to unseen ones. On LCO, every gradient-trained per-image aggregator we test drops $0.39$ to $0.57$ F1 from the in-distribution to the held-out split, a systematic open-world recognition failure in the aggregator, not the visual representation. A linear probe of thirteen different encoders over the same features spreads only about six percentage points of F1 across general-purpose and biomedical pretraining objectives, confirming the representation is sound. We propose three lightweight $\textit{anchor-based}$ decoders that capture per-species presence geometrically over a shared frozen tile-feature pool, scoring $\textit{higher}$ on held-out combinations than on in-distribution validation.
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