Author

Keith G. Mills

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#machine learning Preprint Aug 2026

Segmentation of Bovid Dentition Under Imperfect Annotations: A Comparative Study of Convolutional and Attention Models

This paper presents a comparative study of segmentation architectures, ranging from convolutional backbones to vision transformers, applied to the B.O.V.I.D. dataset, a corpus of high-resolution bovid dental photographs paired with hand-made segmentation masks not originally designed for ML-based training, and evaluates a range of preprocessing and alignment techniques to mitigate the resulting label imperfections.

Keith G. Mills, Evan B. Sanders, Gregory J. Matthews et al. · 0 citations

Jul 2026

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

Study of the efficacy of GNN layers in a slew of regression contexts from rank ordering, error minimization and insight extraction shows that deep convolutional GNNs, particularly GEN, are more effective at these tasks than attention-based GNNs, while other classical, theoretically-inspired GNNs remain competitive and efficient.

Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim · arXiv.org · 0 citations