Classification of histopathology images using ConvNets to detect Lupus Nephritis
This work addresses the need for faster and more efficient diagnosis of Lupus Nephritis in medical pathology, though it is incremental as it builds on existing computational techniques.
The authors tackled the problem of automating the classification of histopathology images for Lupus Nephritis detection by proposing a deep learning pipeline that detects glomeruli patterns and classifies images, achieving automation of a traditionally time-consuming process.
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.