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

Source: [Reliability Assessment Framework Based on Feature Separability for Pathological Cell Image Classification under Prior Bias](<https://www.sota2.com/research/paper/690ad9e25fbd540384004541>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-07T05:02:31.033Z

Authors: Takaaki Tachibana, Toru Nagasaka, Yukari Adachi, Hiroki Kagiyama, Ryota Ito, Mitsugu Fujita, Kimihiro Yamashita, Yoshihiro Kakeji

Published: 2025-11-03T14:33:11.000Z

## Abstract

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$\\times$). 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 ($\\beta = 1.650$, $p \< 0.001$), showing 412-fold stronger impact than prior bias ($\\beta = 0.004$, $p = 0.018$). GAM analysis showed strong predictive power ($R^2 = 0.876$) 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.01953>)

[PDF](<https://arxiv.org/pdf/2511.01953.pdf>)