Predicting Ethanol Drinking Status from Nonhuman Primate Liver Histology: A Proof of Concept for Parameter-efficient Fine-tuning in Few-Shot Vision-Language Modeling
Amirmohammad Mohammadi, Rupak Khadka, Kathleen A. Grant, Mary Lauren Benton, Joshua Peeples
Combining vision-language models (VLMs) with parameter-efficient fine-tuning (PEFT) offers a promising approach to few-shot whole-slide image (WSI) classification in computational pathology. This study evaluates this approach for predicting ethanol drinking status from nonhuman primate liver histology. Adapters are evaluated against non-PEFT configurations on a subset of the Monkey Alcohol Tissue Research Resource (MATRR). All evaluated PEFT configurations achieve higher mean balanced accuracy than the non-PEFT baselines. For example, in the four-shot setting, attention-based multiple instance learning (ABMIL) alone achieves 43.5% mean balanced accuracy, compared with 78.1% after adding an adapter. Qualitative attention analysis shows that textual pathway adaptations produce localized attention heatmaps, while visual pathway adaptations produce more diffuse ones. A pathologist's review identified similar histological features across adaptations.
Future dynamic novel-view synthesis requires extrapolating multiple motion regimes within the same scene. A single dynamics model may not simultaneously represent stationary regions, coordinated motion, local interactions, and articulated trajectories. We introduce Motion Regime Expert Routing for Gaussian Splatting (MoRE-GS), a causal, render-aware framework for forecasting dynamic 3D Gaussian scenes. MoRE-GS freezes a Gaussian representation reconstructed exclusively from the observed temporal prefix and forecasts its control-keypoint motion using four complementary experts: persistence, a coefficient-space Transformer, a graph convolutional network, and a structured Transformer with rigid and non-rigid motion constraints. A compact routing table assigns expert mixtures across soft motion groups and forecast-horizon bins, while a persistence gate suppresses unreliable extrapolation. Across D-NeRF and HyperNeRF scenes, MoRE-GS achieves improved performance against other methods, and these results demonstrate that renderer-calibrated composition of complementary motion predictors improves causal dynamic Gaussian forecasting across heterogeneous motion regimes. Project page: https://advanced-vision-and-learning-lab.github.io/MoRE-GS-website/