VPNet: Tractable and Explainable Classification with Probabilistic Circuits on ViT Features

2025-10-03 radiology and imaging Title + abstract only

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We propose a novel explainable AI (XAI) model for classification tasks that can treat multi-modal and multidimensional information. Unlike traditional classification models based on convolutional neural networks or transformers, our approach combines probabilistic circuits with two vision transformers, DINO and CLIP, enabling probabilistic interpretability at the patch level, encoder level, and modality level. To demonstrate this capability, we developed a three-dimensional multimodal classifica...

VPNet: Tractable and Explainable Classification with Probabilistic Circuits on ViT Features

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