用理论保障的AI皮肤癌筛查系统，准确又可信。
CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening
- 融合统计学习、拓扑分析与贝叶斯推断，构建可解释模型
- 在三个数据集上实现高精度分类且预测概率校准良好
- 适合临床部署，为医疗AI提供可信推理框架
皮肤病影像和移动诊断工具的快速发展，亟需兼具实证性能与强理论保障的系统。深度学习虽具高预测精度，但常缺乏可靠不确定性估计，难以用于临床。为此，我们提出基于统计学习理论、拓扑数据分析（TDA）与贝叶斯共形推断的联合框架——共形贝叶斯皮肤科分类器（CBDC）。该框架提供依赖分布的泛化界，反映皮肤病变变异特性；证明了拓扑稳定性定理，确保卷积神经网络嵌入在光照与形态扰动下保持不变；并给出有限样本下的共形覆盖保证，实现可信赖的不确定性量化。在HAM10000、PH2和ISIC 2020数据集上的大量实验表明，CBDC不仅达到高分类精度，还生成从临床视角可解释的校准预测。本研究推动了深度皮肤病诊断的理论与实践突破，打通机器学习理论与临床应用的接口。
原文摘要 · Abstract (English)
The rapid growth of dermatological imaging and mobile diagnostic tools calls for systems that not only demonstrate empirical performance but also provide strong theoretical guarantees. Deep learning models have shown high predictive accuracy; however, they are often criticized for lacking well, calibrated uncertainty estimates without which these models are hardly deployable in a clinical setting. To this end, we present the Conformal Bayesian Dermatological Classifier (CBDC), a well, founded framework that combines Statistical Learning Theory, Topological Data Analysis (TDA), and Bayesian Conformal Inference. CBDC offers distribution, dependent generalization bounds that reflect dermatological variability, proves a topological stability theorem that guarantees the invariance of convolutional neural network embeddings under photometric and morphological perturbations and provides finite conformal coverage guarantees for trustworthy uncertainty quantification. Through exhaustive experiments on the HAM10000, PH2, and ISIC 2020 datasets, we show that CBDC not only attains classification accuracy but also generates calibrated predictions that are interpretable from a clinical perspective. This research constitutes a theoretical and practical leap for deep dermatological diagnostics, thereby opening the machine learning theory clinical applicability interface.
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