Automatic fine-grained glomerular lesion recognition in kidney pathology

作者：

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.

摘要

•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.

论文关键词：Deep convolutional neural network,Glomerulus segmentation,Fine-grained lesion classification,Uncertainty assessment,Kidney pathology

论文评审过程：Received 29 October 2021, Revised 15 February 2022, Accepted 11 March 2022, Available online 12 March 2022, Version of Record 19 March 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108648