The research team, led by Luke Borst of Antech Diagnostics and collaborators at Mars Petcare Science and Diagnostics, WALTHAM Petcare Science Institute, and Mars digital technology groups, developed and validated a deep learning system called OncoPetNet. The system is an ensemble of three fully convolutional neural networks built on encoder-decoder architectures with skip connections, a design descended from the U-Net family of image segmentation models. Rather than detecting mitotic figures directly as objects, the model frames the problem as segmentation: it predicts a pixel mask marking candidate dividing cells, and deterministic post-processing steps then convert those masks into coordinates. The encoders draw on EfficientNet-b5, EfficientNet-b3, and SE-ResNext backbones pre-trained on ImageNet, allowing the network to leverage general visual features learned from millions of natural images before fine-tuning on tumor histology.

Deploying such a model in a high-volume diagnostic laboratory required more than a clever network. The team built a production pipeline that runs asynchronously on every whole slide image uploaded to the lab. Each slide first passes through a biopsy slide classifier that decides whether mitotic counting is even appropriate. A non-model tissue detection algorithm then maps the foreground tissue and generates coordinates for the mitotic figure detector, which analyzes overlapping tiles across the entire tumor area. Morphological filters sharpen the predicted masks before a k-d tree based algorithm searches all possible placements of a 2.37 square millimeter square and selects the one containing the most mitotic figures. The final output, delivered to pathologists as annotations on the digital slide, includes both the region of interest and circles around each candidate mitosis.

Validation rested on two ground truth datasets. The first comprised 72 soft tissue sarcomas from the lab’s routine caseload, each confirmed by three pathologists, with consensus expert opinion establishing which AI-flagged candidates were true mitoses. On this dataset, OncoPetNet achieved an F1 score of 0.86, with recall, or sensitivity, reaching 0.97. The second benchmark was the MIDOG++ dataset, a publicly available multi-domain collection built specifically to test how well mitosis-detection models generalize across scanners, stains, laboratories, and tumor types. On 100 canine soft tissue sarcoma images from that set, the model scored an F1 of 0.70, with precision of 0.81 but recall dropping to 0.62. The authors attribute the difference partly to domain shift, since their model was trained on their own scanners and staining protocols, and partly to the low mitotic density of many MIDOG++ images, where a single missed mitosis can crater the score.

With the model validated, the team ran two evaluation studies. The first mimicked routine diagnostics: a single pathologist graded all 72 tumors three ways, manually, with AI assistance, and using AI alone. The results were striking. The AI-only condition averaged 19.9 mitotic figures per case, the AI-assisted condition 14, and the manual condition just 6.26. In other words, AI assistance allowed the pathologist to find about 2.24 times as many mitoses as unaided observation. The pathologist’s role shifted from exhaustive searching to verification: rejecting false positives, which the model produced more often than false negatives, and adding back any true mitoses the network missed. Human oversight measurably moderated the AI’s counts, which is precisely the behavior a safety-conscious deployment would want.

Those increased counts rippled through the grading scheme. Mitotic counts are binned into an ordinal MC score from 1 to 3, which is then summed with scores for tumor necrosis and degree of differentiation to produce the final grade. In the first study, AI-assisted scores migrated upward by one level in 29 percent of cases and by two levels in 6 percent compared with manual counts. Tumor grades followed: 18 percent of tumors moved from grade 1 to grade 2, and 7 percent from grade 2 to grade 3. No tumor migrated two full grades, and no tumor moved downward. The second study, in which nine pathologists organized into three cohorts each reviewed 24 cases both manually and with AI assistance, replicated these findings almost exactly. Across 216 paired observations, AI assistance produced an average of 11.6 mitoses versus 5.31 manually, again a roughly 2.2-fold increase, with grade migration of 15.7 percent from grade 1 to 2 and 5.6 percent from grade 2 to 3.

Efficiency gains were equally consistent. Manual counts took an average of 62.8 seconds per case, while AI-assisted counts took 33.7 seconds, nearly a 1.86-fold reduction. In a busy diagnostic laboratory processing thousands of submissions, halving the time spent on one of the most tedious steps in tumor grading represents a substantial operational improvement. Just as importantly, the AI’s pre-screening of every slide and field relieves pathologists of the cognitive burden of an unhurried, exhaustive search, a practice the authors note is technically required for accuracy but impractical under real-world workloads.

The most consequential findings concern agreement between pathologists. Using intraclass correlation coefficients, the team compared how consistently the nine pathologists rated the same tumors under manual versus AI-assisted conditions. For raw mitotic counts, AI assistance significantly improved agreement in two of the three pathologist trios and overall, lifting the overall coefficient from 0.94 to 0.98. Agreement on MC scores also improved significantly overall. Agreement on which slide to select, measured with Fleiss’ Kappa, rose in all three groups and reached statistical significance in two, reflecting the fact that AI effectively removes the guesswork about where to count. Agreement on final tumor grade showed a consistent upward trend, including a substantial jump in one group from 0.48 to 0.72, but these gains did not reach statistical significance. The authors attribute this dilution to the two remaining subjective parameters, percent necrosis and degree of differentiation, which AI does not yet assess and which continue to drive inter-pathologist variability.

The study’s authors are careful about what these results do and do not prove. The clinical significance of the higher, more sensitive counts remains unexplored: it is possible that current MC score thresholds, calibrated on imperfect manual counts, should be adjusted upward now that detection is better, or that AI-assisted grades will ultimately predict biological behavior more accurately. Testing that will require studies correlating AI-assisted grades with patient outcomes, ideally including clinical follow-up and necropsy data. Extending the approach to quantify necrosis and differentiation objectively would complete the standardization picture. There are also inherent limits to ground truth itself, since even expert consensus on what constitutes a mitotic figure carries some subjectivity, and immunohistochemical markers like phosphorylated histone H3 introduce their own interpretive complications.

What the study does establish is a working template for human-AI collaboration in diagnostic pathology. The machine does what machines do best, exhaustively scanning every slide and field without fatigue, while the human does what humans do best, exercising judgment to reject imposters such as apoptotic bodies, pyknotic nuclei, and inflammatory cells that mimic mitoses. The result is a count that is more sensitive, faster, and more reproducible than either party achieves alone. For veterinary oncology, where soft tissue sarcomas are among the most common canine tumors and grading decisions directly guide surgery and follow-up, that combination could mark the beginning of a genuine standardization of tumor grading, one algorithm-assisted count at a time.

Subject of Research: AI-assisted mitotic counting for standardizing histologic grading of canine soft tissue sarcomas

Article Title: Artificial intelligence-assisted mitotic counts improve efficiency, accuracy, and inter-pathologist agreement: a method toward canine soft tissue sarcoma grade standardization

Article References: Borst, L., Bacmeister, C., Yau, W., Aceino, A., Ranck, R., Labelle, P., Ehrensing, G., Oliveira, F., Myers, C., Willcox, J. L., Ellerby, Z., Haydock, R., Taylor, K., Parkinson, M., Fitzke, M., & Gerdin, J. (2025). Artificial intelligence-assisted mitotic counts improve efficiency, accuracy, and inter-pathologist agreement: a method toward canine soft tissue sarcoma grade standardization. Veterinary Oncology, 2(1), Article 19. https://doi.org/10.1186/s44356-025-00034-3

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00034-3

Keywords: artificial intelligence, deep learning, veterinary oncology, soft tissue sarcoma, mitotic count, tumor grading, digital pathology, inter-observer agreement, canine cancer, convolutional neural networks, histopathology, MIDOG++