Model weights for Lymphoid Aggregates Segmentation (in Pytorch 1.0.1)

Allograft rejection is a major concern in kidney transplantation.
Inflammatory processes in patients with kidney allografts involve various
patterns of immune cell recruitment and distributions. Lymphoid aggregates
(LAs) are commonly observed in patients with kidney allografts and their
presence and localization may correlate with severity of acute rejection.
Alongside with other markers of inflammation, LAs assessment is currently
performed by pathologists manually in a qualitative way, which is both
time consuming and far from precise. Here we present the first automated
method of identifying LAs and measuring their densities in whole slide
images of transplant kidney biopsies. We trained a deep convolutional
neural network based on U-Net on 44 core needle kidney biopsy slides,
monitoring loss on a validation set (n=7 slides). The model was
subsequently tested on a hold-out set (n=10 slides). We found that the
coarse pattern of LAs localization agrees between the annotations and
predictions, which is reflected by high correlation between the annotated
and predicted fraction of LAs area per slide (Pearson R of 0.9756).
Furthermore, the network achieves an auROC of 97.78 ± 0.93% and an IoU
score of 69.72 ± 6.24 % per LA-containing slide in the test set. Our study
demonstrates that a deep convolutional neural network can accurately
identify lymphoid aggregates in digitized histological slides of kidney.
This study presents a first automatic DL-based approach for quantifying
inflammation marks in allograft kidney, which can greatly improve
precision and speed of assessment of allograft kidney biopsies when
implemented as a part of computer-aided diagnosis system.