Recognition of glomeruli lesions is the key for diagnosis and treatment
planning in kidney pathology; however, the coexisting glomerular structures
such as mesangial regions exacerbate the difficulties of this task. In this
paper, we introduce a scheme to recognize fine-grained glomeruli lesions from
whole slide images. First, a focal instance structural similarity loss is
proposed to drive the model to locate all types of glomeruli precisely. Then an
Uncertainty Aided Apportionment Network is designed to carry out the
fine-grained visual classification without bounding-box annotations. This
double branch-shaped structure extracts common features of the child class from
the parent class and produces the uncertainty factor for reconstituting the
training dataset. Results of slide-wise evaluation illustrate the effectiveness
of the entire scheme, with an 8-22% improvement of the mean Average Precision
compared with remarkable detection methods. The comprehensive results clearly
demonstrate the effectiveness of the proposed method.

本文提出了一种从全幅切片图像中识别细粒度肾小球病变的方案，通过一个聚焦实例结构相似性 loss 来驱动模型精确定位所有类型的肾小球，设计了一种不需要边界框注释的不确定性辅助配应网络来进行细粒度视觉分类，结果表明，相比较其他显著检测方法，在幻灯片评估中平均精度优化率为 8-22% 的提高，全面证明了该方法的有效性。