On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote. However such a strategy ignores the difference between the grader expertness. In this paper, we consider the task of predicting the segmentation with the calibrated inter-observer uncertainty. We note that in clinical practice, the medical image segmentation is usually used to assist the disease diagnosis. Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis First segmentation Framework (DiFF) is proposed to estimate diagnosis-first segmentation from the raw images.Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis First Ground-truth (DF-GT).Then, we further propose Take and Give Modelto segment DF-GT from the raw image. We verify the effectiveness of DiFF on three different medical segmentation tasks: OD/OC segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF is able to significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.
翻译:在医学图像上,许多组织/分解可能含混不清。 这就是为什么医疗分解通常由一组临床专家加注,以减少个人偏差。 但是, 这种临床常规也给机器学习算法的应用带来了新的挑战。 没有明确的地面真相, 很难训练和评价深层学习模型。 当从不同的年级收集说明时, 通常的选择是多数票。 但是这样的战略忽略了分级专家之间的差别。 在本文中, 我们考虑的是以校准的跨服务器图像不确定性来预测分解的任务。 我们注意到, 在临床实践中, 通常使用医学图像分解来帮助疾病诊断。 在这种观察的启发下, 我们提出诊断第一原则, 就是将疾病诊断作为标准来校准观察者之间的分解。 在此想法之后, 提议了一个名为 Diagnation first 一级分解框架(DIFF) 来估算原始图像的诊断- 一级分解 。 在原始图像中, DiFF 将首先学习将多位分解剖结果整合为 。