  Multi-class cell segmentation in high-resolution Giga-pixel whole slide
images (WSI) is critical for various clinical applications. Training such an AI
model typically requires labor-intensive pixel-wise manual annotation from
experienced domain experts (e.g., pathologists). Moreover, such annotation is
error-prone when differentiating fine-grained cell types (e.g., podocyte and
mesangial cells) via the naked human eye. In this study, we assess the
feasibility of democratizing pathological AI deployment by only using lay
annotators (annotators without medical domain knowledge). The contribution of
this paper is threefold: (1) We proposed a molecular-empowered learning scheme
for multi-class cell segmentation using partial labels from lay annotators; (2)
The proposed method integrated Giga-pixel level molecular-morphology
cross-modality registration, molecular-informed annotation, and
molecular-oriented segmentation model, so as to achieve significantly superior
performance via 3 lay annotators as compared with 2 experienced pathologists;
(3) A deep corrective learning (learning with imperfect label) method is
proposed to further improve the segmentation performance using partially
annotated noisy data. From the experimental results, our learning method
achieved F1 = 0.8496 using molecular-informed annotations from lay annotators,
which is better than conventional morphology-based annotations (F1 = 0.7015)
from experienced pathologists. Our method democratizes the development of a
pathological segmentation deep model to the lay annotator level, which
consequently scales up the learning process similar to a non-medical computer
vision task. The official implementation and cell annotations are publicly
available at https://github.com/hrlblab/MolecularEL.
