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arXiv Computer Vision By Jai Kumar Sharma, Peeyush Tapadiya

Can You Trust Frozen Hematology Foundation Models under Acquisition Shift?

Read the original on arXiv Computer Vision →

The study evaluates 15 frozen hematology foundation-model embeddings across four single‑cell acquisition domains, finding that while in‑domain accuracy is near‑saturated (macro‑F1 0.98–0.997), cross‑dataset performance drops dramatically (34–72%) and model rankings shift. Probe‑dependent rank transfer is observed, with 1‑NN retrieval more stable than linear heads, yet neither reliably predicts target robustness. Calibration deteriorates off‑domain (ECE rises from 0.004 to 0.35), and exposure to internal cohorts confounds shift analysis; a training‑free pseudo‑label‑balanced feature normalization (CBR) modestly improves target‑prior robustness and calibration. whyItMatters":"The findings highlight that frozen hematology foundation models, though accurate in‑domain, may fail under realistic scanner, site, and class‑prior shifts, underscoring the need for comprehensive audits of accuracy, calibration, exposure, and robustness before clinical deployment."

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