Medical image grounding aims to align natural language phrases with specific regions in medical images, serving as a foundational task for intelligent diagnosis, visual question answering (VQA), and automated report generation(MRG). However, existing research is constrained by limited modality coverage, coarse-grained annotations, and the absence of a unified, generalizable grounding framework. To address these challenges, we construct a large-scale medical grounding dataset Med-GLIP-5M comprising over 5.3 million region-level annotations across seven imaging modalities, covering diverse anatomical structures and pathological findings. The dataset supports both segmentation and grounding tasks with hierarchical region labels, ranging from organ-level boundaries to fine-grained lesions. Based on this foundation, we propose Med-GLIP, a modality-aware grounding framework trained on Med-GLIP-5M. Rather than relying on explicitly designed expert modules, Med-GLIP implicitly acquires hierarchical semantic understanding from diverse training data-enabling it to recognize multi-granularity structures, such as distinguishing lungs from pneumonia lesions. Extensive experiments demonstrate that Med-GLIP consistently outperforms state-of-the-art baselines across multiple grounding benchmarks. Furthermore, integrating its spatial outputs into downstream tasks, including medical VQA and report generation, leads to substantial performance gains. Our dataset is available at Venn2025/Med-GLIP-5M.

Sand dust weather has adverse effects on image quality, making single-image sand dust removal a classic research topic in the field of image restoration. However, existing learning-based image restoration methods fail to account for uncertainties in both data and model dimensions, thus being unable to produce satisfactory results for sand dust image restoration. To address this challenge, we introduce a novel framework called the Uncertainty-aware SAM-aided Prompt-interaction Network (USPNet). USPNet comprises two key modules: the Uncertainty-aware SAM Priors Module (USPM), which addresses data-wise aleatoric uncertainties, and the Uncertainty-aware Prompt Learning Module (UPLM), which tackles model-wise epistemic uncertainties. By integrating data-wise and model-wise uncertainty learning, USPNet leverages uncertainty modeling through SAM semantic priors and distributionally representative prompts. Recognizing the unexplored uncertainties inherent in the learning process, we propose an Uncertainty-aware Perceptual Loss (UPL) to enhance the visual quality of restored images through perceptual learning. Through comprehensive perceptual studies and analysis of real sand-dust images, we propose a dataset named SanddustClearity. SanddustClearity includes daytime, nighttime synthetic, and real-world sand dust images. Our extensive experiments, conducted on both synthetic and real-world images exhibiting various levels of sand dust degradation, confirm the effectiveness and robustness of our proposed method. Our code will be available at https://github.com/WBC-ML/USPNet.

Medical Visual Question Answering (Med-VQA) holds significant potential for clinical decision support, yet existing efforts primarily focus on 2D imaging with limited task diversity. This paper presents 3D-RAD, a large-scale dataset designed to advance 3D Med-VQA using radiology CT scans. The 3D-RAD dataset encompasses six diverse VQA tasks: anomaly detection, image observation, medical computation, existence detection, static temporal diagnosis, and longitudinal temporal diagnosis. It supports both open- and closed-ended questions while introducing complex reasoning challenges, including computational tasks and multi-stage temporal analysis, to enable comprehensive benchmarking. Extensive evaluations demonstrate that existing vision-language models (VLMs), especially medical VLMs exhibit limited generalization, particularly in multi-temporal tasks, underscoring the challenges of real-world 3D diagnostic reasoning. To drive future advancements, we release a high-quality training set 3D-RAD-T of 136,195 expert-aligned samples, showing that fine-tuning on this dataset could significantly enhance model performance. Our dataset and code, aiming to catalyze multimodal medical AI research and establish a robust foundation for 3D medical visual understanding, are publicly available.

Accurately determining the clinical positions for each tooth is essential in orthodontics, while most existing solutions heavily rely on inefficient manual design. In this paper, we present the LETA, a dual-branch Latent Encoding based 3D Tooth Alignment. Our system takes as input the segmented individual 3D tooth meshes in the Intra-oral Scanner (IOS) dental surfaces, and automatically predicts the proper 3D pose transformation for each tooth. LETA includes three components: an Encoder that learns a latent code of dental pointcloud, a Projector that transforms the latent code of misaligned teeth to predicted aligned ones, and a Solver to estimate the transformation between different dental latent codes. A key novelty of LETA is that we extract the features from the ground truth (GT) aligned teeth to guide network learning during training. To effectively learn tooth features, our Encoder employs an improved point-wise convolutional operation and an attention-based network to extract local shape features and global context features respectively. Extensive experimental results on a large-scale dataset with 9,868 IOS surfaces demonstrate that LETA can achieve state-of-the-art performance. A further clinical applicability study reveals that our method can reduce orthodontists' workload over 60% compared to starting tooth alignment from scratch, demonstrating the strong potential of deep learning for future digital dentistry.

General-purpose Large Language Models (LLMs) like GPT-4 have achieved remarkable advancements in machine translation (MT) by leveraging extensive web content.On the other hand, translation-specific LLMs are built by pre-training on domain-specific monolingual corpora and fine-tuning with human-annotated translation data.Despite the superior performance, these methods either demand an unprecedented scale of computing and data or substantial human editing and annotation efforts.In this paper, we develop MT-Ladder, a novel model-agnostic and cost-effective tool to refine the performance of general LLMs for MT.MT-Ladder is trained on pseudo-refinement triplets which can be easily obtained from existing LLMs without additional human cost.During training, we propose a hierarchical finetuning strategy with an easy-to-hard schema, improving MT-Ladder's refining performance progressively.The trained MT-Ladder can be seamlessly integrated with any general-purpose LLMs to boost their translation performance.By utilizing Gemma-2B/7B as the backbone, MT-Ladder-2B can elevate raw translations to the level of top-tier open-source models (e.g., refining BigTranslate-13B with +6.91 BLEU and +3.52 COMET for XX→En), and MT-Ladder-7B can further enhance model performance to be on par with the state-of-theart GPT-4.Extensive ablation and analysis corroborate the effectiveness of MT-Ladder in diverse settings.Our code is available at https://github.com/fzp0424/MT-Ladder.