  Membranous nephropathy (MN) is a frequent type of adult nephrotic syndrome,
which has a high clinical incidence and can cause various complications. In the
biopsy microscope slide of membranous nephropathy, spikelike projections on the
glomerular basement membrane is a prominent feature of the MN. However, due to
the whole biopsy slide contains large number of glomeruli, and each glomerulus
includes many spike lesions, the pathological feature of the spikes is not
obvious. It thus is time-consuming for doctors to diagnose glomerulus one by
one and is difficult for pathologists with less experience to diagnose. In this
paper, we establish a visualized classification model based on the multi-scale
annotation multi-instance learning (MSA-MIL) to achieve glomerular
classification and spikes visualization. The MSA-MIL model mainly involves
three parts. Firstly, U-Net is used to extract the region of the glomeruli to
ensure that the features learned by the succeeding algorithm are focused inside
the glomeruli itself. Secondly, we use MIL to train an instance-level
classifier combined with MSA method to enhance the learning ability of the
network by adding a location-level labeled reinforced dataset, thereby
obtaining an example-level feature representation with rich semantics. Lastly,
the predicted scores of each tile in the image are summarized to obtain
glomerular classification and visualization of the classification results of
the spikes via the usage of sliding window method. The experimental results
confirm that the proposed MSA-MIL model can effectively and accurately classify
normal glomeruli and spiked glomerulus and visualize the position of spikes in
the glomerulus. Therefore, the proposed model can provide a good foundation for
assisting the clinical doctors to diagnose the glomerular membranous
nephropathy.
