Vision-language models remain underused in colonoscopy despite the rich expert descriptions recorded in routine reports. These reports document lesion appearance, size and location but summarise entire procedures rather than caption individual frames, leaving clinical findings only weakly linked to the corresponding images. Here we develop EndoCLIP, a colonoscopy vision-language foundation model trained on 125,756 lesion-level image-text pairs progressively recovered from 280,476 routine colonoscopy records. Across lesion-level image-text retrieval, structured report generation and six multi-centre clinical classification tasks, EndoCLIP outperforms general-purpose and biomedical vision-language encoders in both zero-shot and linear-probe settings. On benign-versus-malignant classification, its linear probe approaches the performance of expert readers in a blinded study involving 12 endoscopists. These results suggest that recovering finding-to-frame correspondence can transform routine documentation into scalable supervision, enabling clinical targets to be specified in language rather than separately annotated for each task.
Pre-trained tabular prediction models based on Prior-Data Fitted Networks (PFNs), such as TabPFN and LimiX, have achieved remarkable progress in supervised learning, demonstrating immense potential across real-world scenarios and diverse downstream tasks. However, a critical question remains systematically unexplored: Does pre-training on data generated via causal mechanisms truly endow models with the ability to comprehend underlying causal structures? Furthermore, can these models leverage such causal information to achieve stable prediction across environments? To address these fundamental questions, we propose StablePFN, a novel tabular foundation model that integrates explicit causal awareness with stable predictive modeling. Leveraging a key yet largely overlooked advantage of the PFN paradigm, the availability of ground-truth causal structure during synthetic data generation, we train StablePFN to jointly identify the Markov Boundary (MB) of the target variable and perform the primary prediction task. We introduce an end-to-end ''Decouple-Discover-Predict'' architecture that utilizes sample decoupling weights to guide MB discovery, and employs a hard attention masking mechanism during inference to incorporate causal structural knowledge. Extensive experiments on both synthetic and real-world benchmarks demonstrate that StablePFN significantly outperforms state-of-the-art baselines in cross-environment prediction settings, particularly in challenging high-bias scenarios.
Zheng Guan, Yikang Chen, Hao Qian et al.· Proceedings of the 32nd ACM...· 0 citations