Interpretable Point Cloud Classification Using Multiple Instance Learning

Understanding 3D cell shape is crucial in biomedical research, where morphology serves as a key indicator of disease, cellular state, and drug response. However, many existing 3D point cloud classification models lack interpretability, limiting their utility for extracting biologically meaningful insights. In this work, we unify standard point cloud backbones and feature aggregation strategies within a Multiple Instance Learning (MIL) framework to enable inherently interpretable classification. Our approach, POINT-MIL, improves classification performance while providing fine-grained point-level explanations without relying on post hoc analysis. We demonstrate state-of-the-art mACC (97.3%) and F1 (97.5%) in the IntrA biomedical dataset and evaluate the interpretability using quantitative and qualitative metrics. Additionally, we introduce ATLAS-1, a novel dataset of drug-treated 3D cancer cells, and use it to show how POINTMIL captures fine-grained morphological effects of chemical treatments. Beyond biomedical applications, POINTMIL generalises to standard benchmarks such as ModelNet40 and ScanObjectNN, offering interpretable 3D object recognition across domains 1 .