# Classification of histopathology images using ConvNets to detect Lupus Nephritis

arXiv: https://arxiv.org/abs/2112.07555 · HTML: https://mlsanity.com/paper/2112.07555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation](https://mlsanity.com/paper/2101.07654/) (2021)

## Built on by (most cited first, 1 of 1)

- [Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology](https://mlsanity.com/paper/2301.13128/) (2023)

## Abstract (arXiv)

Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in renal failure due to these attacks. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) has released a classification system based on various patterns observed during renal injury in SLE. Traditional methods require meticulous pathological assessment of the renal biopsy and are time-consuming. Recently, computational techniques have helped to alleviate this issue by using virtual microscopy or Whole Slide Imaging (WSI). With the use of deep learning and modern computer vision techniques, we propose a pipeline that is able to automate the process of 1) detection of various glomeruli patterns present in these whole slide images and 2) classification of each image using the extracted glomeruli features.