Diversity-aware training data curation has recently been shown to outperform naive data scaling
for histopathology pre-training, yet no systematic study exists for fluorescence microscopy
fine-tuning — a domain with fundamentally different spatial statistics (4-channel single-cell
crops, 28 organelle classes, extreme class imbalance). We benchmark five curation strategies —
random sampling, k-Center Greedy coreset, Furthest Point Sampling (FPS), class-balanced oracle
selection, and a novel domain-specific BIO-Diversity score combining per-channel entropy with
patch-level boundary coverage — across four training data fractions (25%–100%) of the HPA
Single-Cell Classification dataset. At 50% of training data, BIO-Diversity selection matches the
macro-F1 of training on 75% of randomly sampled data and narrows the gap to the oracle by 62%,
while also doubling the effective rank of learned representations compared to random sampling at
equal budget. Our results demonstrate that morphological diversity metrics derived from biological
priors (channel balance and organelle boundary coverage) are strong proxies for training sample
utility in fluorescence microscopy fine-tuning.
Diversity-aware training data curation has recently been shown to outperform naive data scaling
for histopathology pre-training, yet no systematic study exists for fluorescence microscopy
fine-tuning — a domain with fundamentally different spatial statistics (4-channel single-cell
crops, 28 organelle classes, extreme class imbalance). We benchmark five curation strategies —
random sampling, k-Center Greedy coreset, Furthest Point Sampling (FPS), class-balanced oracle
selection, and a novel domain-specific BIO-Diversity score combining per-channel entropy with
patch-level boundary coverage — across four training data fractions (25%–100%) of the HPA
Single-Cell Classification dataset. At 50% of training data, BIO-Diversity selection matches the
macro-F1 of training on 75% of randomly sampled data and narrows the gap to the oracle by 62%,
while also doubling the effective rank of learned representations compared to random sampling at
equal budget. Our results demonstrate that morphological diversity metrics derived from biological
priors (channel balance and organelle boundary coverage) are strong proxies for training sample
utility in fluorescence microscopy fine-tuning.
Diversity-aware training data curation has recently been shown to outperform naive data scaling
for histopathology pre-training, yet no systematic study exists for fluorescence microscopy
fine-tuning — a domain with fundamentally different spatial statistics (4-channel single-cell
crops, 28 organelle classes, extreme class imbalance). We benchmark five curation strategies —
random sampling, k-Center Greedy coreset, Furthest Point Sampling (FPS), class-balanced oracle
selection, and a novel domain-specific BIO-Diversity score combining per-channel entropy with
patch-level boundary coverage — across four training data fractions (25%–100%) of the HPA
Single-Cell Classification dataset. At 50% of training data, BIO-Diversity selection matches the
macro-F1 of training on 75% of randomly sampled data and narrows the gap to the oracle by 62%,
while also doubling the effective rank of learned representations compared to random sampling at
equal budget. Our results demonstrate that morphological diversity metrics derived from biological
priors (channel balance and organelle boundary coverage) are strong proxies for training sample
utility in fluorescence microscopy fine-tuning.
