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Holistic Evaluation of GPT-4V for Biomedical Imaging

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arXiv 2023年

作者： Liu, Zhengliang Jiang, Hanqi Zhong, Tianyang Wu, Zihao Ma, Chong Li, Yiwei Yu, Xiaowei Zhang, Yutong Pan, Yi Shu, Peng Lyu, Yanjun Zhang, Lu Yao, Junjie Dong, Peixin Cao, Chao Xiao, Zhenxiang Zhao, Huan Wang, Jiaqi Xu, Shaochen Wei, Yaonai Chen, Jingyuan Wang, Peilong Dai, Haixing He, Hao Wang, Zewei Wang, Xinyu Zhang, Xu Zhao, Lin Liu, Yiheng Zhang, Kai Yan, Zhiling Liu, Jun Sun, Lichao Qiang, Ning Ge, Bao Cai, Xiaoyan Zhao, Shijie Hu, Xintao Yuan, Yixuan Li, Gang Zhang, Shu Zhang, Xin Jiang, Xi Zhang, Tuo Shen, Dinggang Li, Quanzheng Liu, Wei Li, Xiang Zhu, Dajiang Liu, Tianming School of Computing University of Georgia GA United States School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China School of Automation Northwestern Polytechnical University Xi'An710072 China Department of Computer Science and Engineering University of Texas ArlingtonTX United States Institute of Medical Research Northwestern Polytechnical University Xi'An710072 China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science Northwestern Polytechnical University Xi'An710072 China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States School of Physics and Information Technology Shaanxi Normal University Xi'An710119 China Department of Computer Science and Engineering Lehigh University PA United States Department of Radiology Second Xiangya Hospital Central South University Changsha410011 China Department of Electronic Engineering Chinese University of Hong Kong 999077 Hong Kong Department of Radiology and BRIC University of North Carolina Chapel HillNC United States School of Biomedical Engineering ShanghaiTech University Shanghai Clinical Research and Trial Center Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. China Shanghai Clinical Research Trial Center China Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States

In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain. We assess GPT-4V's performance across 16 medical imaging categories, including radiology, oncology, ophthalmology, pathology, and more. Tasks include modality recognition, anatomy localization, disease diagnosis, report generation, and lesion detection. The extensive experiments provide insights into GPT-4V's strengths and weaknesses. Results show GPT-4V's proficiency in modality and anatomy recognition but difficulty with disease diagnosis and localization. GPT-4V excels at diagnostic report generation, indicating strong image captioning skills. While promising for biomedical imaging AI, GPT-4V requires further enhancement and validation before clinical deployment. We emphasize responsible development and testing for trustworthy integration of biomedical AGI. This rigorous evaluation of GPT-4V on diverse medical images advances understanding of multimodal large language models (LLMs) and guides future work toward impactful healthcare applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

Optimization of functional tasks for a vision system of a mobile robot

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Optimization of functional tasks for a vision system of a mo...

2019 International Multi-Conference on engineering, Computer and Information Sciences, SIBIRCON 2019

作者： Topolsky, D.V. Plaksina, Y.G. Topolskaya, I.G. Gonenko, T.V. Topolsky, N.D. Yumagulov, N.I. Chelyabinsk Russia Department of Electronic Computing Machines Chelyabinsk Russia Automation and Robotics Omsk State Technical University Omsk Russia PLANAR Department of Engineering Chelyabinsk Russia Department for Education in Physics and Mathematics Nizhnevartovsk Russia

ISBN: (纸本)9781728144016

This paper analyzes features of a computer vision of mobile robotic systems. One of the features of mobile robot vision systems is the limited computing resources. This is because, due to the requirements for cost and overall characteristics of the studied type of robots, their design uses relatively cheap computing devices with low speed and memory size. Therefore, it is necessary to simplify tasks that we set for the vision system, focusing on existing equipment. In addition, it is necessary to choose correct recognition algorithms for the vision system and adapt them for use in mobile versions of robotic systems. The paper discusses recognition algorithms in terms of requirements for vision systems of mobile robots. On the example of prototypes of mobile robots, optimal combinations of hardware and software solutions are shown, taking into account a simplification of functional tasks of vision systems. It is proposed to provide the requirements for computer vision systems under consideration by a rational combination of software and hardware capabilities. © 2019 IEEE.

关键词： Mobile robots

Efficient incremental model reduction approach for time-varying spatially distributed processes

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Neurocomputing 2025年 643卷

作者： Ling, Ai Ru, Hao Chen, Xueqin Teo, Kok Lay Department of Automation Harbin University of Science and Technology Harbin150086 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6845 Australia Research Center of Satellite Technology Harbin Institute of Technology Harbin150040 China School of Mathematical Sciences Sunway University Selangor Darul Ehsan 47500 Malaysia

The accurate and efficient modeling of complex spatially distributed processes (SDPs) represents a significant challenge for researchers in the field. A variety of modeling approaches have been developed with the objective of addressing issues related to spatiotemporal modeling. The conventional offline modeling approaches necessitate the availability of a spatiotemporal data set that encompasses the entirety of the system's dynamic features. It is not possible to fully satisfy this prerequisite in the context of real-world applications, particularly in the case of time-varying SDPs. Furthermore, the process of updating the model by repeatedly applying the offline algorithm is inherently time-consuming. In this paper, we put forth an efficient incremental model reduction approach for time-varying SDPs. Following the initialization phase, the modified Gram–Schmidt orthogonalization method is employed to extract the feature subspace in a sequential manner. The time-space synthesis is then utilized to reconstruct the spatiotemporal dynamics. The efficacy of the model is evaluated on a representative diffusion-reaction process, and the comparative experimental outcomes demonstrate that the presented algorithm is an efficient and effective approach for the online learning of time-varying SDPs. © 2025 Elsevier B.V.

关键词： Population distribution

A survey of industrial control system testbeds 2

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A survey of industrial control system testbeds

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and automation, AMIMA 2019

作者： Geng, Yangyang Wang, Yi Liu, Wenwen Wei, Qiang Liu, Ke Wu, Haolan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Department of Research and Development Center Beijing Sifang Automation Co. Ltd Beijing100085 China

Industrial Control System(ICS) testbed is the basis of ICS safety research. In order to build an ICS testbed, it is necessary to have a deep understanding of the current research status. This paper introduced the structure and composition of the typical ICS system. Then it simply analyzed the technical characteristics, system framework and advantages and disadvantages of four types of typical ICS testbeds. Finally, it summarized application scenarios of ICS testbed and pointed out some problems in testbed construction and the development direction of ICS testbed with the fusion of cyber and physical. © Published under licence by IOP Publishing Ltd.

关键词： Testbeds

Adaptive cooperative tracking and parameter estimation of an uncertain leader over general directed graphs

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arXiv 2020年

作者： Wang, Shimin Zhang, Hongwei Chen, Zhiyong Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada The School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Guangdong518055 China The School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

MSC Codes 93DxxThis paper studies cooperative tracking problem of heterogeneous Euler-Lagrange systems with an uncertain leader, with emphasis on simultaneous adaptive estimation of the state and parameters of the leader node. The observer design does not rely on the frequency information of the leader node, and the estimation errors are shown to converge to zero exponentially. The results can be applied to general directed graphs, due to two newly developed Lyapunov equations, which solely depend on communication network topologies. Interestingly, using these Lyapunov equations, many results of multi-agent systems over undirected graphs can be extended to general directed graphs. This paper also advances the knowledge base of adaptive control systems by providing a main tool in the analysis of parameter convergence for adaptive observers. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

Evaluating Large Language Models for Radiology Natural Language Processing

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arXiv 2023年

作者： Liu, Zhengliang Zhong, Tianyang Li, Yiwei Zhang, Yutong Pan, Yi Zhao, Zihao Dong, Peixin Cao, Chao Liu, Yuxiao Shu, Peng Wei, Yaonai Wu, Zihao Ma, Chong Wang, Jiaqi Wang, Sheng Zhou, Mengyue Jiang, Zuowei Li, Chunlin Holmes, Jason Xu, Shaochen Zhang, Lu Dai, Haixing Zhang, Kai Zhao, Lin Chen, Yuanhao Liu, Xu Wang, Peilong Yan, Pingkun Liu, Jun Ge, Bao Sun, Lichao Zhu, Dajiang Li, Xiang Liu, Wei Cai, Xiaoyan Hu, Xintao Jiang, Xi Zhang, Shu Zhang, Xin Zhang, Tuo Zhao, Shijie Li, Quanzheng Zhu, Hongtu Shen, Dinggang Liu, Tianming School of Computing University of Georgia GA United States School of Automation Northwestern Polytechnical University Xi'An710072 China Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Institute of Medical Research Northwestern Polytechnical University Xi'An710072 China School of Physics and Information Technology Shaanxi Normal University Xi'An710119 China School of Computer Science Northwestern Polytechnical University Xi'An710072 China School of Biomedical Engineering ShanghaiTech University Shanghai Clinical Research and Trial Center Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. China Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Biomedical Engineering Center for Biotechnology Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY12180 United States Department of Radiology Second Xiangya Hospital Changsha410011 China Shanghai Clinical Research Trial Center China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering Lehigh University PA United States Department of Linguistics Department of Computer Science Dartmouth College NH United States Lingang Laboratory Shanghai200031 China

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain. © 2023, CC BY.

关键词： Radiology

A New Hardware Approach to Self-Organizing Maps

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A New Hardware Approach to Self-Organizing Maps

International Conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Leonardo A. Dias Maria G. F. Coutinho Elena Gaura Marcelo A. C. Fernandes Laboratory of Machine Learning Federal University of Rio Grande do Norte Natal Brazil Faculty of Engineering and Computing Coventry University Coventry United Kingdom Department of Computer and Automation Engineering Coventry University Coventry United Kingdom

ISBN: (数字)9781728171470

ISBN: (纸本)9781728172798

Self-Organizing Maps (SOMs) are widely used as a data mining technique for applications that require data dimensionality reduction and clustering. Given the complexity of the SOM learning phase and the massive dimensionality of many data sets as well as their sample size in Big Data applications, high-speed processing is critical when implementing SOM approaches. This paper proposes a new hardware approach to SOM implementation, exploiting parallelization, to optimize the system’s processing time. Unlike most implementations in the literature, this proposed approach allows the parallelization of the data dimensions instead of the map, ensuring high processing speed regardless of data dimensions. An implementation with field-programmable gate arrays (FPGA) is presented and evaluated. Key evaluation metrics are processing time (or throughput) and FPGA area occupancy (or hardware resources).

关键词： Neurons Field programmable gate arrays Self-organizing feature maps Hardware Measurement Proposals Complexity theory

All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

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arXiv 2021年

作者： Xu, Zhe Wang, Yixin Lu, Donghuan Yu, Lequan Yan, Jiangpeng Luo, Jie Ma, Kai Zheng, Yefeng Tong, Raymond Kai-Yu Department of Biomedical Engineering The Chinese University of Hong Kong Shatin NT Hong Kong Tencent Jarvis Lab Shenzhen China Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Department of Automation Tsinghua University Beijing China Brigham and Women's Hospital Harvard Medical School BostonMA United States

Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for their superior performance, wherein the real labels are only utilized to supervise their paired images via supervised loss while the unlabeled images are exploited by enforcing the perturbation-based "unsupervised" consistency without explicit guidance from those real labels. However, intuitively, the expert-examined real labels contain more reliable supervision signals. Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training? To this end, we discard the previous perturbation-based consistency but absorb the essence of non-parametric prototype learning. Based on the prototypical networks, we then propose a novel cyclic prototype consistency learning (CPCL) framework, which is constructed by a labeled-to-unlabeled (L2U) prototypical forward process and an unlabeled-to-labeled (U2L) backward process. Such two processes synergistically enhance the segmentation network by encouraging more discriminative and compact features. In this way, our framework turns previous "unsupervised" consistency into new "supervised" consistency, obtaining the "all-around real label supervision" property of our method. Extensive experiments on brain tumor segmentation from MRI and kidney segmentation from CT images show that our CPCL can effectively exploit the unlabeled data and outperform other state-of-the-art semisupervised medical image segmentation methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Image segmentation

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

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arXiv 2020年

作者： Hao, Xiaotian Peng, Zhaoqing Ma, Yi Wang, Guan Jin, Junqi Hao, Jianye Chen, Shan Bai, Rongquan Xie, Mingzhou Xu, Miao Zheng, Zhenzhe Yu, Chuan Li, Han Xu, Jian Gai, Kun College of Intelligence and Computing Tianjin University Tianjin China Alimama Alibaba Group Beijing China Department of Automation Tsinghua University Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In E-commerce, advertising is essential for merchants to reach their target users. The typical objective is to maximize the advertiser’s cumulative revenue over a period of time under a budget constraint. In real applications, an advertisement (ad) usually needs to be exposed to the same user multiple times until the user finally contributes revenue (e.g., places an order). However, existing advertising systems mainly focus on the immediate revenue with single ad exposures, ignoring the contribution of each exposure to the final conversion, thus usually falls into suboptimal solutions. In this paper, we formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space while ensuring the solution quality. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach. Extensive offline and online experiments show the superior performance of our approaches over state-of-the-art baselines in terms of cumulative revenue. Copyright © 2020, The Authors. All rights reserved.

关键词： Marketing