We present MIRA (Multimodal Interventional RAdiology evaluation), a comprehensive benchmark for evaluating large multimodal models in expert-level interventional radiology tasks requiring specialized domain knowledge and advanced visual reasoning capabilities. Unlike existing medical benchmarks that primarily provide binary labels without contextual depth, MIRA offers diverse question formats, including open-ended, closed-ended, single-choice, and multiple-choice categories, each accompanied by detailed expert-validated explanations. The benchmark incorporates approximately 184K high-quality medical images spanning multiple imaging modalities with 1.2M meticulously generated question-answer pairs across various anatomical regions. These pairs were created through a sophisticated cascade methodology involving expert interventional radiologists at both the data collection and validation stages. Our comprehensive evaluation, encompassing zero-shot testing and fine-tuning experiments of large multimodal models, revealing significant performance gaps between AI systems and human specialists. Fine-tuning experiments demonstrate substantial improvements, with models achieving up to 0.80 accuracy on single-choice questions. MIRA establishes a challenging benchmark that suggests promising directions for developing specialized clinical AI systems for interventional radiology.

The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and whole-slide image (WSI)-level models, limiting the integration of learned knowledge across patches and WSIs and resulting in redundant models. In this work, we introduce CPath-Omni, the first 15B parameter LMM that unifies patch and WSI analysis, consolidating a variety of tasks at both levels, including classification, visual question answering, captioning, and visual referring prompting. Extensive experiments demonstrate that CPath-Omni achieves state-of-the-art (SOTA) performance across seven diverse tasks on 39 out of 42 datasets, outperforming or matching task-specific models trained for individual tasks. Additionally, we develop a specialized pathology CLIP-based visual processor for CPath-Omni, CPath-CLIP, which, for the first time, integrates different vision models and incorporates a large language model as a text encoder to build a more powerful CLIP model, which achieves SOTA performance on nine zero-shot and four few-shot datasets. Our findings highlight CPath-Omni’s ability to unify diverse pathology tasks, demonstrating its potential to streamline and advance the field of foundation model in pathology. The code and model are available at CPath-Omni.

Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Slide Image (WSI) analysis. Additionally, they can function as vision encoders when combined with large language models (LLMs) to support broader capabilities. Current efforts to train pathology VLMs rely on pathology image-text pairs from platforms like PubMed, YouTube, and Twitter, which provide limited, unscalable data with generally suboptimal image quality. In this work, we leverage large-scale WSI datasets like TCGA to extract numerous high-quality image patches. We then train a large multimodal model (LMM) to generate captions for extracted images, creating PathGen-1.6M, a dataset containing 1.6 million high-quality image-caption pairs. Our approach involves multiple agent models collaborating to extract representative WSI patches, generating and refining captions to obtain high-quality image-text pairs. Extensive experiments show that integrating these generated pairs with existing datasets to train a pathology-specific CLIP model, PathGen-CLIP, significantly enhances its ability to analyze pathological images, with substantial improvements across nine pathology-related zero-shot image classification tasks and three whole-slide image tasks. Furthermore, we construct 200K instruction-tuning data based on PathGen-1.6M and integrate PathGen-CLIP with the Vicuna LLM to create more powerful multimodal models through instruction tuning. Overall, we provide a scalable pathway for high-quality data generation in pathology, paving the way for next-generation general pathology models. Our dataset, code, and model are open-access at https://github.com/PathFoundation/PathGen-1.6M.

Recent advances in computational pathology have led to the emergence of numerous foundation models. These models typically rely on general-purpose encoders with multi-instance learning for whole slide image (WSI) classification or apply multimodal approaches to generate reports directly from images. However, these models cannot emulate the diagnostic approach of pathologists, who systematically examine slides at low magnification to obtain an overview before progressively zooming in on suspicious regions to formulate comprehensive diagnoses. Instead, existing models directly output final diagnoses without revealing the underlying reasoning process. To address this gap, we introduce CPathAgent, an innovative agent-based approach that mimics pathologists' diagnostic workflow by autonomously navigating across WSI through zoom-in/out and move operations based on observed visual features, thereby generating substantially more transparent and interpretable diagnostic summaries. To achieve this, we develop a multi-stage training strategy that unifies patch-level, region-level, and WSI-level capabilities within a single model, which is essential for replicating how pathologists understand and reason across diverse image scales. Additionally, we construct PathMMU-HR², the first expert-validated benchmark for large region analysis. This represents a critical intermediate scale between patches and whole slides, reflecting a key clinical reality where pathologists typically examine several key large regions rather than entire slides at once. Extensive experiments demonstrate that CPathAgent consistently outperforms existing approaches across benchmarks at three different image scales, validating the effectiveness of our agent-based diagnostic approach and highlighting a promising direction for computational pathology.

Rapid advancements in large multimodal models (LMMs) have significantly enhanced their applications in pathology, particularly in image classification, pathology image description, and whole slide image (WSI) classification. In pathology, WSIs represent gigapixel-scale images composed of thousands of image patches. Therefore, both patch-level and WSI-level evaluations are essential and inherently interconnected for assessing LMM capabilities. In this work, we propose PathBench, which comprises three subsets at both patch and WSI levels, to refine and enhance the validation of LMMs. At the patch-level, evaluations using existing multi-choice Q&A datasets reveal that some LMMs can predict answers without genuine image analysis. To address this, we introduce PatchVQA, a large-scale visual question answering (VQA) dataset containing 5,382 images and 6,335 multiple-choice questions designed with distractor options to prevent shortcut learning. These new questions are rigorously validated by professional pathologists to ensure reliable model assessments. At the WSI-level, current efforts primarily focus on image classification tasks and lack diverse validation datasets for multimodal models. To address this, we generate a detailed WSI report dataset through an innovative approach that integrates detailed patch descriptions generated by foundational models into comprehensive WSI reports. These are then combined with physician-written reports corresponding to TCGA WSIs, resulting in WSICap, a detailed report dataset containing 7,000 samples. Based on WSICap, we further develop a WSI-level VQA dataset, WSIVQA, to serve as a validation set for WSI LMMs. Using these PathBench subsets, we conduct extensive experiments to benchmark the performance of state-of-the-art LMMs at both the patch and WSI levels. The proposed dataset is available at https://github.com/superjamessyx/PathBench.

3D Human pose estimation from multiple cameras with unknown calibration has received less attention than it should. The few existing data-driven solutions do not fully exploit 3D training data that are available on the market, and typically train from scratch for every novel multi-view scene, which impedes both accuracy and efficiency. We show how to exploit 3D training data to the fullest and associate multiple dynamic views efficiently to achieve high precision on novel scenes using a simple yet effective framework, dubbed Multiple Dynamic View Pose estimation (MDVPose). MDVPose utilizes novel scenarios data to finetune a single-view pretrained motion encoder in multi-view setting, aligns arbitrary number of views in a unified coordinate via Procruste alignment, and imposes multi-view consistency. The proposed method achieves 22.1 mm P-MPJPE or 34.2 mm MPJPE on the challenging in-the-wild Ski-Pose PTZ dataset, which outperforms the state-of-the-art method by 24.8% P-MPJPE (-7.3 mm) and 19.0% MPJPE (-8.0 mm). It also outperforms the state-of-the-art methods by a large margin (-18.2mm P-MPJPE and -28.3mm MPJPE) on the EgoBody dataset. In addition, MDVPose achieves robust performance on the Human3.6M datasets featuring multiple static cameras. Code is available at https://github.com/iGame-Lab/MDVPose.