A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study

The pathology of animal studies is crucial for toxicity
evaluations
and regulatory assessments, but the manual examination of slides by
pathologists remains time-consuming and requires extensive training.
One inherent challenge in this process is the interobserver variability,
which can compromise the consistency and accuracy of a study. Artificial
intelligence (AI) has demonstrated its ability to automate similar
examinations in clinical applications with enhanced efficiency, consistency,
and accuracy. However, training AI models typically relies on costly
pixel-level annotation of injured regions and is often not available
for animal pathology. To address this, we developed the PathologAI
system, a “weakly” supervised approach for WSI classification
in rat images without explicit lesion annotation at the pixel level.
Using rat liver imaging data from the Open TG-GATEs system, PathologAI
was applied to predict necrosis of n = 816 WSIs (377
controls). TG-GATEs studied 170 compounds at three dose levels (low,
middle, and high) for four time points (3, 7, 14, and 28 days). PathologAI
first preprocessed WSIs at the tile level to generate a high-level
representation with a Generative Adversarial Network architecture.
The prediction of liver necrosis relied on an ensemble model of 5
CNN classifiers trained on 335 WSIs. The ensemble model achieved notable
classification accuracy on the holdout test set: 87% among 87 control
slides free of findings, 83% among 120 controls with spontaneous necrosis,
67% among 147 treated animals with spontaneous minimal or slight necrosis,
and 59% among 127 treated animals with minimal or slight necrosis
caused by the treatment. Importantly, PathologAI was able to discriminate
WSIs with spontaneous necrosis from those with treatment related necrosis
and discriminated mild lesion level findings (slight vs minimal) and
between treatment dose levels. PathologAI could provide an inexpensive
and rapid screening tool to assist the digital pathology analysis
in preclinical applications and general toxicological studies.