Demir Ugur, Irmakci Ismail, Keles Elif, Topcu Ahmet, Xu Ziyue, Spampinato Concetto, Jambawalikar Sachin, Turkbey Evrim, Turkbey Baris, Bagci Ulas
Department of Radiology and BME, Northwestern University, Chicago, IL, USA.
ECE, Ege University, Izmir, Turkey.
Mach Learn Med Imaging. 2021 Sep;12966:396-405. doi: 10.1007/978-3-030-87589-3_41. Epub 2021 Sep 21.
Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods. The source code will be publicly available upon publication.
可视化解释方法在注释数据有限或不可用的患者预后中具有重要作用。已经有几次尝试使用基于梯度的归因方法从医学扫描中定位病变，而不使用分割标签。由于缺乏鲁棒性和可靠性，这一研究方向受到了阻碍。这些方法对网络参数高度敏感。在本研究中，我们引入了一种鲁棒的可视化解释方法来解决医学应用中的这一问题。我们提供了一种通用的创新可视化解释算法，并作为示例应用，展示了其在不使用密集分割标签的情况下，高精度且鲁棒地量化新冠病毒导致的肺部病变的有效性。这种方法克服了常用的Grad-CAM及其扩展版本的缺点。我们提出的策略背后的前提是，在确保分类器预测保持相似的同时，信息流被最小化。我们的研究结果表明，瓶颈条件比类似的归因方法提供了更稳定的严重程度估计。源代码将在发表后公开提供。
