■我们研究的目的是通过称为置信校准标签平滑(CC-LS)损失的专门损失函数来增强用于青光眼分类的机器学习模型的校准。这种方法专门设计用于通过集成标签平滑和置信度惩罚技术来优化模型校准而不影响准确性,针对青光眼检测的具体情况量身定制。
■本研究的重点是校准后的深度学习模型的开发和评估。
■该研究采用了来自两个外部数据集的眼底图像-用于青光眼分析和研究的在线视网膜眼底图像数据库(482正常,168青光眼)和视网膜眼底青光眼挑战(720正常,80个青光眼)-和一个广泛的内部数据集(每个类别4639张图像),旨在增强模型的通用性。使用综合测试集(47.913正常,1629青光眼)来自内部数据集。
■CC-LS损失函数无缝集成了标签平滑,这缓和了极端的预测,以避免过度拟合,基于信任的惩罚。这些惩罚阻止了该模型对不正确的分类表示不适当的信心。我们的研究旨在使用CC-LS训练模型,并将其性能与使用常规损失函数训练的模型进行比较。
■使用Brier分数等指标评估模型的精度,灵敏度,特异性,和假阳性率,与定性热图分析一起进行整体准确性评估。
■初步研究结果表明,采用CC-LS机制的模型具有出色的校准指标,Brier得分为0.098,以及显着的准确性指标:灵敏度为81%,特异性为80%,和80%的加权精度。重要的是,在不牺牲分类精度的情况下实现校准中的这些增强。
■CC-LS损失函数在寻求将机器学习模型用于青光眼诊断方面取得了重大进展。通过改进校准,CC-LS确保临床医生可以解释和信任预测概率,使人工智能驱动的诊断工具更具临床可行性。从临床的角度来看,这种增强的信任和可解释性可能导致更及时和适当的干预,从而优化患者的预后和安全性。
■专有或商业披露可在本文末尾的脚注和披露中找到。
UNASSIGNED: The aim of our research is to enhance the calibration of machine learning models for glaucoma classification through a specialized loss function named Confidence-Calibrated Label Smoothing (CC-LS) loss. This approach is specifically designed to refine model calibration without compromising accuracy by integrating label smoothing and confidence penalty techniques, tailored to the specifics of glaucoma detection.
UNASSIGNED: This study focuses on the development and evaluation of a calibrated deep learning model.
UNASSIGNED: The study employs fundus images from both external datasets-the Online Retinal Fundus Image Database for Glaucoma Analysis and Research (482 normal, 168 glaucoma) and the Retinal Fundus Glaucoma Challenge (720 normal, 80 glaucoma)-and an extensive internal dataset (4639 images per category), aiming to bolster the model\'s generalizability. The model\'s clinical performance is validated using a comprehensive test set (47 913 normal, 1629 glaucoma) from the internal dataset.
UNASSIGNED: The CC-LS loss function seamlessly integrates label smoothing, which tempers extreme predictions to avoid overfitting, with confidence-based penalties. These penalties deter the model from expressing undue confidence in incorrect classifications. Our study aims at training models using the CC-LS and comparing their performance with those trained using conventional loss functions.
UNASSIGNED: The model\'s precision is evaluated using metrics like the Brier score, sensitivity, specificity, and the false positive rate, alongside qualitative heatmap analyses for a holistic accuracy assessment.
UNASSIGNED: Preliminary findings reveal that models employing the CC-LS mechanism exhibit superior calibration metrics, as evidenced by a Brier score of 0.098, along with notable accuracy measures: sensitivity of 81%, specificity of 80%, and weighted accuracy of 80%. Importantly, these enhancements in calibration are achieved without sacrificing classification accuracy.
UNASSIGNED: The CC-LS loss function presents a significant advancement in the pursuit of deploying machine learning models for glaucoma diagnosis. By improving calibration, the CC-LS ensures that clinicians can interpret and trust the predictive probabilities, making artificial intelligence-driven diagnostic tools more clinically viable. From a clinical standpoint, this heightened trust and interpretability can potentially lead to more timely and appropriate interventions, thereby optimizing patient outcomes and safety.
UNASSIGNED: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.