  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.
