论文标题
从二元分类模型中提取脑肿瘤ROI的全局扰动攻击
Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
论文作者
论文摘要
深度学习技术极大地使计算机辅助诊断系统受益。但是，与其他领域不同，在医学成像中，由于手动注释和隐私法规的高成本，获得了大型细粒注释数据集（例如3D肿瘤分割），这具有挑战性。这使人们对弱点的方法进行了兴趣，以利用弱标记的数据进行肿瘤分割。在这项工作中，我们提出了一种弱监督的方法，以使用二进制类标签获得利益区域。此外，我们提出了一个新颖的目标函数，以基于预验证的二元分类模型来训练发电机模型。最后，我们将方法应用于MRI中的脑肿瘤分割问题。
Deep learning techniques have greatly benefited computer-aided diagnostic systems. However, unlike other fields, in medical imaging, acquiring large fine-grained annotated datasets such as 3D tumour segmentation is challenging due to the high cost of manual annotation and privacy regulations. This has given interest to weakly-supervise methods to utilize the weakly labelled data for tumour segmentation. In this work, we propose a weakly supervised approach to obtain regions of interest using binary class labels. Furthermore, we propose a novel objective function to train the generator model based on a pretrained binary classification model. Finally, we apply our method to the brain tumour segmentation problem in MRI.
