Correlation-Guided Network for Fine-Grained Classification of Glomerular lesions in Kidney Histopathology Images

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul:2020:5781-5784. doi: 10.1109/EMBC44109.2020.9176234.

Abstract

Chronic Kidney Disease has become a worldwide public health problem which demands careful assessments by pathologists. In this paper, we propose a novel architecture for fine-grained classification of glomerular lesions in renal pathology images sampling from patients with IgA nephropathy. The adversarial correlation loss is innovatively presented to guide a parallel convolutional neural network. In this well- designed loss function, bias between the prediction and the label was take into account while the relationship among different categories is well-aligned. Glomerular lesions in this study are divided into five subcategories, Neg (Negative samples such as tubule and artery), SS (sclerosis involving a portion of the glomerular tuft), GS (sclerosis involving 100% of the tuft), C (build-up of more than two layers of cells within Bowman's space, often with fibrin and collagen deposition) and NOA (none of above). Our model with 93.0% accuracy and 92.9% Fl-score for these five categories has proved superior to other models through experimental results.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Glomerulonephritis, IGA* / pathology
  • Humans
  • Kidney / pathology
  • Kidney Glomerulus / pathology
  • Renal Insufficiency, Chronic* / pathology
  • Sclerosis / pathology