Automatic fine-grained glomerular lesion recognition in kidney pathology. (July 2022)

• Record Type: Journal Article
• Authors: Nan, Yang; Li, Fengyi; Tang, Peng; Zhang, Guyue; Zeng, Caihong; Xie, Guotong; Liu, Zhihong; Yang, Guang
• Is Part Of: Pattern recognition. Volume 127(2022)
• Publication Date: 2022-07
• Subjects: Deep convolutional neural network -- Glomerulus segmentation -- Fine-grained lesion classification -- Uncertainty assessment -- Kidney pathology; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108648
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper proposed an efficient scheme for fine-grained lesion recognition in kidney pathology to help pathologists make more objective and effective clinical diagnoses. The proposed method has improved segmentation performance by optimizing the structural integrity through instance. The proposed method has addressed the fine-grained lesion classification by incorporating uncertainty assessment and data reconstitution, without any bounding box annotation or adversarial model. Abstract: 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. Graphical abstract: Image, graphical abstract .