Application of a convolutional neural network for automatic cytological diagnosis of 7 common canine tumors on whole slide images: a proof-of-concept

Background Artificial intelligence may help improve speed and consistency of diagnostic veterinary cytology, but validated tools remain limited. Objectives To develop and evaluate a convolutional neural network (CNN) for automatic classification of 7 common canine tumors on cytology whole-slide images (WSIs). Methods Ninety-one May–Grünwald Giemsa–stained canine cytology cases were selected, representing carcinoma, histiocytoma, lymphoma, mast cell tumor, melanoma, plasma cell tumor, and soft tissue sarcoma, together with a background class. Slides were tiled into 22,577 image patches (512 × 512 pixels) and manually labeled and split using a case-wise 3-fold design to avoid data leakage. An ImageNet-pretrained Inception-v3 CNN was fine-tuned for 8-class classification. Performance was assessed at tile level, Grad-CAM was used for qualitative interpretability analysis, and majority-vote prediction was further evaluated at ROI level using attention-guided inference and at WSI level using attention-free whole-slide inference. Results Mean test accuracy at tile level was 86.3%, with mean macro balanced accuracy of 92.0% and mean macro F1-score of 85.6%. Grad-CAM showed attention focused predominantly on cytologically relevant cellular regions. At ROI level, attention-guided inference achieved 100% Top1 accuracy (28/28 cases). At WSI level, attention-free whole-slide inference achieved 92.9% Top1 accuracy (26/28 cases), and the correct diagnosis was included within the top 3 predictions in all cases. Conclusion Inception-v3 achieved high diagnostic performance on canine cytology WSIs. The results support the feasibility of both AI-assisted ROI-based decision support and fully automated whole-slide inference in veterinary cytology. These findings are proof-of-concept evidence from a curated, single-institution cohort of typical, well-differentiated cases and require validation on diagnostically challenging and external cases before clinical use.