基于阴道镜图像的可解释注意力模型用于宫颈癌前病变风险分类
An Explainable Attention Model for Cervical Precancer Risk Classification using Colposcopic Images
AI总结：
本文提出Cervix-AID-Net模型，结合CBAM与四种可解释技术对阴道镜图像进行宫颈癌前病变风险分类，准确率达99%以上，展示了其作为辅助评估工具的潜力。
摘要：
宫颈癌仍然是全球重大的健康问题，早期识别和风险评估在有效的预防干预中起着关键作用。本文提出了用于宫颈癌前病变风险分类的Cervix-AID-Net模型。该研究基于患者的阴道镜图像设计并评估了所提出的Cervix-AID-Net模型。该模型包含卷积块注意力模块（CBAM）和卷积层，用于提取阴道镜图像的可解释且具有代表性的特征，以区分高风险和低风险宫颈癌前病变。此外，所提出的Cervix-AID-Net模型集成了四种可解释技术，即基于输出特征图和输入特征的梯度类激活图、局部可解释模型无关解释、CartoonX和像素率失真解释。使用留出法和十折交叉验证技术进行评估，分类准确率分别达到99.33%和99.81%。分析表明，CartoonX能够提供图像中相关的分段平滑部分，因此为Cervix-AID-Net模型的决策提供了细致的解释。输入中高斯噪声和模糊的影响表明，在高达3%的高斯噪声和10%的模糊情况下，性能保持不变，此后性能下降。将所提出模型的性能与其他深度学习方法进行比较的研究凸显了Cervix-AID-Net模型作为提高宫颈癌前病变风险评估有效性的辅助工具的潜力。所提出的方法结合了CBAM和可解释人工智能集成，有潜力影响宫颈癌预防和早期检测，改善患者预后，并降低这种可预防疾病在全球的负担。
英文摘要：
Cervical cancer remains a major worldwide health issue, with early identification and risk assessment playing critical roles in effective preventive interventions. This paper presents the Cervix-AID-Net model for cervical precancer risk classification. The study designs and evaluates the proposed Cervix-AID-Net model based on patients colposcopy images. The model comprises a Convolutional Block Attention Module (CBAM) and convolutional layers that extract interpretable and representative features of colposcopic images to distinguish high-risk and low-risk cervical precancer. In addition, the proposed Cervix-AID-Net model integrates four explainable techniques, namely gradient class activation maps, Local Interpretable Model-agnostic Explanations, CartoonX, and pixel rate distortion explanation based on output feature maps and input features. The evaluation using holdout and ten-fold cross-validation techniques yielded a classification accuracy of 99.33\% and 99.81\%. The analysis revealed that CartoonX provides meticulous explanations for the decision of the Cervix-AID-Net model due to its ability to provide the relevant piece-wise smooth part of the image. The effect of Gaussian noise and blur on the input shows that the performance remains unchanged up to Gaussian noise of 3\% and blur of 10\%, while the performance reduces thereafter. A comparison study of the proposed model's performance compared to other deep learning approaches highlights the Cervix-AID-Net model's potential as a supplemental tool for increasing the effectiveness of cervical precancer risk assessment. The proposed method, which incorporates the CBAM and explainable artificial integration, has the potential to influence cervical cancer prevention and early detection, improving patient outcomes and lowering the worldwide burden of this preventable disease.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
