通过风险分析提升乳腺癌分型预测准确率，解决模型误判问题。
Adaptive Deep Learning for Breast Cancer Subtype Prediction Via Misprediction Risk Analysis
- 基于多类别误判风险分析，用可解释特征评估预测置信度。
- 在多个数据集上使F1分数提升至61.15%~80.53%，显著降低误判率。
- 适合临床部署，尤其适用于标签少、数据分布不均的场景。
乳腺癌仍是全球癌症死亡的主要原因。早期检测至关重要，但人工组织病理学分析复杂且存在观察者间差异。尽管基于深度神经网络的诊断系统在二分类任务中取得进展，但在多类分型预测中仍面临类间相似性高、类别不平衡和领域偏移等问题，导致频繁误判。本文提出MultiRisk框架，通过量化与缓解乳腺癌分型预测中的误判风险，实现自适应学习。该框架采用多类别误判风险分析模型，利用异构DNN表示提取可解释特征，对误判可能性进行排序，并训练专用风险模型捕捉多类风险模式。在此基础上，设计基于风险的自适应训练策略，根据数据集特性微调预测模型，有效降低误判风险并增强对不同工作负载的适应性。在多个组织病理图像数据集上验证，风险分析的AUROC达78.1%、75.6%、76.3%；风险驱动的自适应训练进一步将F1分数提升至61.15%、65.98%、80.53%，展现出在不同分辨率和领域偏移下的有效性。该方法结合误判风险分析与自适应微调，提升了预测精度，缓解了小样本条件下的误差，并在不同领域、癌症类型和模型架构间具有良好泛化能力，支持可靠临床决策。代码已开源：https://github.com/SheerazNWPU/MultiRisk
原文摘要 · Abstract (English)
Breast cancer remains a leading cause of cancer-related mortality worldwide. Early detection is critical, yet manual histopathology analysis is complex and subject to inter-observer variability. While deep neural network-based diagnostic systems have advanced binary tasks, they struggle with multiclass subtype prediction due to inter-class similarity, class imbalance, and domain shifts, resulting in frequent mispredictions. This study proposes MultiRisk, an adaptive learning framework that quantifies and mitigates misprediction risk in breast cancer subtype prediction from histopathology images. MultiRisk employs a multiclass misprediction risk analysis model that ranks misprediction likelihood using interpretable features derived from heterogeneous DNN representations, with a dedicated risk model trained to capture multiclass risk patterns. Building on this, we introduce a risk-based adaptive learning strategy that fine-tunes prediction models based on dataset-specific characteristics, effectively reducing misprediction risk and improving adaptability to diverse workloads. The framework is evaluated on multiple histopathological image datasets, achieving AUROCs of 78.1%, 75.6%, and 76.3% for risk analysis. Risk-based adaptive training further improves F1-scores to 61.15%, 65.98%, and 80.53%, demonstrating effectiveness across resolutions and domain shifts. By combining misprediction risk analysis with adaptive fine-tuning, MultiRisk improves predictive accuracy, mitigates errors under limited labeled data, and generalizes across domains, cancer types, and model architectures, supporting reliable clinical decision-making. Code: https://github.com/SheerazNWPU/MultiRisk
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。