Despite significant advancements in general AI, its effectiveness in the medical domain is limited by the lack of specialized medical knowledge. To address this, we formulate GMAI-VL-5.5M, a multimodal medical dataset created by converting hundreds of specialized medical datasets with various annotations into high-quality image-text pairs. This dataset offers comprehensive task coverage, diverse modalities, and rich image-text data. Building upon this dataset, we develop GMAI-VL, a 7B-parameter general medical vision-language model, with a three-stage training strategy that enhances the integration of visual and textual information. This approach significantly improves the model's ability to process multimodal data, supporting accurate diagnoses and clinical decision-making. Experiments show that GMAI-VL achieves state-of-the-art performance across various multimodal medical tasks, including visual question answering and medical image diagnosis.

Despite the progress made by multimodal large language models (MLLMs) in computational pathology, they remain limited by a predominant focus on patch-level analysis, missing essential contextual information at the whole-slide level. The lack of large-scale instruction datasets and the gigapixel scale of whole slide images (WSIs) pose significant developmental challenges. In this paper, we present SlideChat, the first vision-language assistant capable of understanding gigapixel whole-slide images, exhibiting excellent multimodal conversational capability and response complex instruction across diverse pathology scenarios. To support its development, we created SlideInstruction, the largest instruction-following dataset for WSIs consisting of 4.2K WSI captions and 176K VQA pairs with multiple categories. Furthermore, we propose SlideBench, a multimodal benchmark that incorporates captioning and VQA tasks to assess SlideChat’s capabilities in various settings such as microscopy, diagnosis and clinical. Compared to both general and specialized MLLMs, SlideChat exhibits exceptional capabilities, achieving state-of-the-art performance on 18 of 22 tasks. For example, it achieved an overall accuracy of 81.17% on SlideBench-VQA (TCGA), and 54.15% on SlideBench-VQA (BCNB). Our code, data, and model is publicly accessible at https://uni-medical.github.io/SlideChat.github.io.

Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks—an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.

This paper introduces MCTrack, a new 3D multi-object tracking method that achieves performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we introduce a novel set of metrics designed to evaluate the output of motion information, including velocity and acceleration, which are essential for subsequent tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack

Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited clinical relevance, incomplete evaluations, and insufficient guidance for interactive LVLMs. To address these limitations, we developed the GMAI-MMBench, the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format. Additionally, we implemented a lexical tree structure that allows users to customize evaluation tasks, accommodating various assessment needs and substantially supporting medical AI research and applications. We evaluated 50 LVLMs, and the results show that even the advanced GPT-4o only achieves an accuracy of 53.96\%, indicating significant room for improvement. Moreover, we identified five key insufficiencies in current cutting-edge LVLMs that need to be addressed to advance the development of better medical applications. We believe that GMAI-MMBench will stimulate the community to build the next generation of LVLMs toward GMAI.

Visual Question Answering (VQA) aims to obtain answers through image-question pairs. Nowadays, the VQA model tends to get answers only through questions, ignoring the information in the images. This phenomenon is caused by bias. As indicated by previous studies, the bias in VQA mainly comes from text modality. Our analysis of bias suggests that the question type is a crucial factor in bias formation. To interrupt the shortcut from question type to answer for de-biasing, we propose a self-supervised method for Shuffling Question Types (SQT) to reduce bias from text modality, which overcomes the prior language problem by mitigating the question-to-answer bias without introducing external annotations. Moreover, we propose a new objective function for negative samples. Experimental results show that our approach can achieve 61.76% accuracy on the VQA-CP v2 dataset, which outperforms the state-of-the-art in both self-supervised and supervised methods.

We propose FlexVAA, a system for identifying roadside infrastructure using flexible, passive wireless retroreflectors. FlexVAA can be easily attached to any surface, allowing roadside infrastructure to be upgraded for autonomous systems without impairing existing operations. Preliminary results show that attaching FlexVAA to a surface reflects more power than without FlexVAA attached. In the future, we plan to arrange multiple FlexVAA elements in order to embed data in the passively reflected signal, allowing identification for multiple unique tags.

A novel LC VCO employing an adaptive body-bias is proposed for low power and low voltage high performance signal generation. By using the adaptive body-bias technique, the threshold voltage of the cross-coupled pair is reduced, and meanwhile the effective source-to-drain voltage and oscillation amplitude are increased, which improves the phase noise. In addition, the back-gate capacitances from the cross-coupled pair are used for frequency tuning which avoids quality factor deteriorations resulted from using extra tuning capacitance. In a standard 65nm CMOS process with a 0.4-V power supply, the proposed VCO achieves FOM of 189.8 with a phase noise of −122.5 dBc/Hz at 10MHz offset when operating at 24.25 GHz. The power consumption of the VCO is 0.94 mW.