Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization. To overcome this problem, we propose a weakly supervised learning-based approach that can effectively learn to localize the discriminative evidence for a diagnostic label from weakly labeled training data. Experimental results show that our proposed method can reliably pinpoint the location of cancerous evidence supporting the decision of interest, while still achieving a competitive performance on glimpse-level and slide-level histopathologic cancer detection tasks.
翻译:尽管深刻的进化神经网络提高了数字病理学分析中图像分类和分解的性能,但它们通常在临床应用的可解释性方面薄弱,或需要大量说明来实现目标定位。为了克服这一问题,我们提议采取监督不力的以学习为基础的方法,能够有效地学习用标签不高的培训数据将诊断标签的歧视性证据本地化。实验结果显示,我们提出的方法可以可靠地确定支持有关决定的癌症证据的位置,同时在视觉水平和幻灯片水平的病理病理学癌症检测任务上仍然取得竞争性的成绩。