Benchmarking Deformable-Attention Graph Networks and Other Aggregators on Prov-GigaPath Features for Canine Cutaneous Tumor Classification

Abstract

Whole-slide image (WSI) classification in veterinary computational pathology usually freezes a human-pretrained pathology foundation model as the tile encoder and trains a light slide-level aggregator on top. Whether the choice of aggregator matters for small canine cohorts has not been tested under a common protocol. On the public CATCH dataset (350 canine cutaneous tumor WSIs, seven subtypes), we compared 14 aggregators that shared frozen Prov-GigaPath tile embeddings, patient-level five-fold splits, and training budgets: multiple-instance learning (MIL) poolings (including ACMIL), static-graph and dynamic-graph neural networks (GNNs, including WiKG), and GigaPath-DefGNN, a coordinate-aware deformable graph-attention aggregator that we adapted to tile point clouds of up to ~94,000 tiles. On the held-out test partitions, pooled over all 350 WSIs, the 14 aggregators spanned a balanced accuracy of 0.889–0.960. GigaPath-DefGNN reached 0.940 (95% confidence interval 0.910–0.967) with best-validation checkpoints and did not differ significantly from any baseline (exact McNemar tests, smallest Holm-adjusted p = 0.148). Its attention maps matched pathologist tumor annotations no better than those of a graph-attention baseline (median Pearson r 0.226 versus 0.224). Validation-set estimates exceeded held-out balanced accuracy by up to 0.066, and the checkpoint-selection rule alone changed held-out accuracy by up to 0.037 and reordered the models: GigaPath-DefGNN ranked eighth under best-validation selection, first under the latest-top-3 rule, and second with the final weights of a re-run of the same configuration. Repeating the headline comparison on 20× tiles did not change these conclusions; among the graph constructions, one post hoc, exploratory variant (GigaPath-DefGNN on a feature k-nearest-neighbor graph) ranked highest. On frozen Prov-GigaPath features for this cohort, the evaluation design mattered as much as the choice of aggregator. Code, splits, and per-WSI predictions will be made publicly available.