Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis

Despite the potential to revolutionise disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel clinical interpretable ConvNet architecture is proposed not only for accurate glaucoma diagnosis but also for the more transparent interpretation by highlighting the distinct regions recognised by the network. To the best of our knowledge, this is the first work of providing the interpretable diagnosis of glaucoma with the popular deep learning model. We propose a novel scheme for aggregating features from different scales to promote the performance of glaucoma diagnosis, which we refer to as M-LAP. Moreover, by modelling the correspondence from binary diagnosis information to the spatial pixels, the proposed scheme generates glaucoma activations, which bridge the gap between global semantical diagnosis and precise location. In contrast to previous works, it can discover the distinguish local regions in fundus images as evidence for clinical interpretable glaucoma diagnosis. Experimental results, performed on the challenging ORIGA datasets, show that our method on glaucoma diagnosis outperforms state-of-the-art methods with the highest AUC (0.88). Remarkably, the extensive results, optic disc segmentation (dice of 0.9) and local disease focus localization based on the evidence map, demonstrate the effectiveness of our methods on clinical interpretability.

尽管通过执行数据驱动的分类有可能彻底改变疾病诊断，但卷积神经网络（ConvNet）的临床可解释性仍然具有挑战性。在本文中，提出了一种新颖的临床可解释卷积神经网络架构，它不仅可用于准确诊断青光眼，还可通过突出网络识别的不同区域来实现更透明的解释。据我们所知，这是首次利用流行的深度学习模型对青光眼进行可解释性诊断的工作。我们提出了一种新的方案，用于聚合不同尺度的特征以提高青光眼诊断的性能，我们将其称为M - LAP。此外，通过对从二元诊断信息到空间像素的对应关系进行建模，所提出的方案生成了青光眼激活图，它弥合了全局语义诊断和精确位置之间的差距。与以往的工作不同，它能够发现眼底图像中可区分的局部区域，作为临床可解释性青光眼诊断的依据。在具有挑战性的ORIGA数据集上进行的实验结果表明，我们的青光眼诊断方法优于最先进的方法，具有最高的曲线下面积（AUC为0.88）。值得注意的是，广泛的结果，包括视盘分割（骰子系数为0.9）以及基于证据图的局部病灶定位，证明了我们的方法在临床可解释性方面的有效性。