# Reliability Assessment Framework Based on Feature Separability for Pathological Cell Image Classification under Prior Bias

arXiv: https://arxiv.org/abs/2511.01953 · HTML: https://mlsanity.com/paper/2511.01953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Revisiting the Role of Foundation Models in Cell-Level Histopathological Image Analysis under Small-Patch Constraints -- Effects of Training Data Scale and Blur Perturbations on CNNs and Vision Transformers](https://mlsanity.com/paper/2603.04081/) (2026)

## Abstract (arXiv)

Background and objective: Prior probability shift between training and deployment datasets challenges deep learning-based medical image classification. Standard correction methods reweight posterior probabilities to adjust prior bias, yet their benefit is inconsistent. We developed a reliability framework identifying when prior correction helps or harms performance in pathological cell image analysis. Methods: We analyzed 303 colorectal cancer specimens with CD103/CD8 immunostaining, yielding 185,432 annotated cell images across 16 cell types. ResNet models were trained under varying bias ratios (1.1-20×). Feature separability was quantified using cosine similarity-based likelihood quality scores, reflecting intra- versus inter-class distinctions in learned feature spaces. Multiple linear regression, ANOVA, and generalized additive models (GAMs) evaluated associations among feature separability, prior bias, sample adequacy, and F1 performance. Results: Feature separability dominated performance (β = 1.650, p < 0.001), showing 412-fold stronger impact than prior bias (β = 0.004, p = 0.018). GAM analysis showed strong predictive power (R2 = 0.876) with mostly linear trends. A quality threshold of 0.294 effectively identified cases requiring correction (AUC = 0.610). Cell types scoring >0.5 were robust without correction, whereas those <0.3 consistently required adjustment. Conclusion: Feature extraction quality, not bias magnitude, governs correction benefit. The proposed framework provides quantitative guidance for selective correction, enabling efficient deployment and reliable diagnostic AI.