Quaxwell

Summary
The paper introduces HyperBank, a compact, interpretable few-shot segmentation model for spheroid microscopy that combines classical image-processing operators. It evaluates the model on three independently acquired datasets and demonstrates that it can be competitive with, and in some cases outperform, larger foundation models, particularly on contrast-driven data.

Mathematical/empirical assessment
HyperBank uses a differentiable bank of classical operators, including Frangi vesselness, Sauvola threshold pyramid, structure-tensor responses, gradient magnitude, and Laplacian-of-Gaussian filters. The model is fitted on annotated support images and evaluated on held-out test images. The loss function combines multiple terms, including binary cross-entropy, Dice loss, focal Tversky loss, and a gradient-based term. Empirical results show that HyperBank achieves strong performance, especially on small-cluster, contrast-driven data, while being significantly smaller in parameters than foundation models.

Strengths
- The model is compact and interpretable, making it suitable for scenarios where transparency is important.
- The ablation studies provide insights into which operators contribute most to performance.
- The evaluation protocol is rigorous, using a strict few-shot setup across multiple datasets.
- The results demonstrate that classical priors can be highly effective in few-shot settings, particularly when the data is driven by contrast and boundary cues.

Concerns
- The paper does not fully address how the model generalizes to other domains or imaging conditions beyond the tested datasets.
- The comparison with foundation models is limited to specific configurations, and the parameter budget differences are not fully balanced.
- Some results, such as those on the heterogeneous \textsc{SpheroidJ} dataset, suggest that learned texture and morphology representations remain superior in certain cases.

Final decision
Strong accept