MindLab Research Group, Universidad Nacional de Colombia, Bogotá, Colombia.
Institute of Information Systems, HES-SO (University of Applied Sciences and Arts Western Switzerland), Sierre, Switzerland.
Comput Biol Med. 2022 Jun;145:105472. doi: 10.1016/j.compbiomed.2022.105472. Epub 2022 Apr 9.
Although for many diseases there is a progressive diagnosis scale, automatic analysis of grade-based medical images is quite often addressed as a binary classification problem, missing the finer distinction and intrinsic relation between the different possible stages or grades. Ordinal regression (or classification) considers the order of the values of the categorical labels and thus takes into account the order of grading scales used to assess the severity of different medical conditions. This paper presents a quantum-inspired deep probabilistic learning ordinal regression model for medical image diagnosis that takes advantage of the representational power of deep learning and the intrinsic ordinal information of disease stages. The method is evaluated on two different medical image analysis tasks: prostate cancer diagnosis and diabetic retinopathy grade estimation on eye fundus images. The experimental results show that the proposed method not only improves the diagnosis performance on the two tasks but also the interpretability of the results by quantifying the uncertainty of the predictions in comparison to conventional deep classification and regression architectures. The code and datasets are available at https://github.com/stoledoc/DQOR.
尽管对于许多疾病都有一个渐进的诊断标准，但基于等级的医学图像的自动分析通常被视为一个二分类问题，忽略了不同可能阶段或等级之间的更细微的区别和内在关系。有序回归（或分类）考虑了类别标签值的顺序，因此考虑了用于评估不同医疗状况严重程度的分级尺度的顺序。本文提出了一种基于量子启发的深度概率学习有序回归模型，用于医学图像诊断，该模型利用了深度学习的表示能力和疾病阶段的内在有序信息。该方法在两个不同的医学图像分析任务上进行了评估：前列腺癌诊断和眼底图像上的糖尿病视网膜病变等级估计。实验结果表明，与传统的深度分类和回归架构相比，该方法不仅提高了两个任务的诊断性能，而且通过量化预测的不确定性，还提高了结果的可解释性。代码和数据集可在 https://github.com/stoledoc/DQOR 上获得。
