  Moving from animal models to human applications in preclinical research
encompasses a broad spectrum of disciplines in medical science. A fundamental
element in the development of new drugs, treatments, diagnostic methods, and in
deepening our understanding of disease processes is the accurate measurement of
kidney tissues. Past studies have demonstrated the viability of translating
glomeruli segmentation techniques from mouse models to human applications. Yet,
these investigations tend to neglect the complexities involved in segmenting
pathological glomeruli affected by different lesions. Such lesions present a
wider range of morphological variations compared to healthy glomerular tissue,
which are arguably more valuable than normal glomeruli in clinical practice.
Furthermore, data on lesions from animal models can be more readily scaled up
from disease models and whole kidney biopsies. This brings up a question:
``\textit{Can a pathological segmentation model trained on mouse models be
effectively applied to human patients?}" To answer this question, we introduced
GLAM, a deep learning study for fine-grained segmentation of human kidney
lesions using a mouse model, addressing mouse-to-human transfer learning, by
evaluating different learning strategies for segmenting human pathological
lesions using zero-shot transfer learning and hybrid learning by leveraging
mouse samples. From the results, the hybrid learning model achieved superior
performance.
