Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the preservation of important diagnostic information. We evaluate our method on videos from fetal ultrasound screening, where commonly only a small amount of the recorded data is used diagnostically. We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Indian Institute of Technology（印度理工学院）；University of Alberta（阿尔伯塔大学）；Hospital Universitario Puerta de Hierro（普埃塔德耶罗大学医院）；Government Medical College（政府医学院）；Sree Chitra Tirunal Institute of Medical Sciences and Technology（斯里奇特拉图鲁纳尔医学科学与技术研究所）；Bhagwan Mahaveer Jain Hospital（巴格万马哈维尔耆那医院）
The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. The proposed video-summarization technique is a step in this direction that provides clinicians access to relevant key-frames from a given ultrasound scan (such as lung ultrasound) while reducing resource, storage and bandwidth requirements. We propose a new unsupervised reinforcement learning (RL) framework with novel rewards that facilitates unsupervised learning avoiding tedious and impractical manual labelling for summarizing ultr…
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
Concordia University（康考迪亚大学）；Ecole de Technologie Superieure (ETS)（高等技术学院）；Laval University（拉瓦尔大学）；Khalifa University（哈利法大学）；Lebanese American University（黎巴嫩美国大学）
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) system to automatically detect landmarks in 3D brain images. C-MARL enables the agents to learn explicit communication channels, as well as implicit communication signals by sharing certain weights of the architecture among all the agents. The proposed approach is evaluated on two brain imaging datasets from adult magnetic resonance imaging (MRI) and fetal ultrasound scans. Our experiments show that involving multiple coo…
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Chung Shan Medical University（中山医学大学）
Clinical finding summaries from an orthopantomogram, or a dental panoramic radiograph, have significant potential to improve patient communication and speed up clinical judgments. While orthopantomogram is a first-line tool for dental examinations, no existing work has explored the summarization of findings from it. A finding summary has to find teeth in the imaging study and label the teeth with several types of past treatments. To tackle the problem, we developDeepOPG that breaks the summarization process into functional segmentation and tooth localization, the latter of which is further ref…
Indian Institute of Technology Palakkad（印度理工学院帕拉卡德分校）；Hospital Universitario Puerta de Hierro（普埃尔塔·德·耶罗大学医院）；Sree Chitra Tirunal Institute for Medical Sciences & Technology（斯里·奇特拉·蒂鲁纳尔医学科学与技术研究所）；Government Medical College Thiruvananthapuram（特里凡得琅政府医学院）
Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of severity of lung into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a sig…
Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders. In this setting it is essential to be able to provide means to obtain diagnostic data to potentially inexperienced users who did not receive an extensive medical training. Interpretation and acquisition of ultrasound images is not trivial. First, the user needs to find a suitable sound window which can be used to get a clear image, and then he needs to correctly interpret it to perform a diagnosis. Althoug…
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames. In this paper, we introduce the 3DST-UNet-RL framework for video summarization. A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). An RL agent learns from spatio-temporal latent scores and predicts actions for keeping or rejecting a video frame in a video summary. We investigate if …
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Queen Mary University of London（伦敦玛丽女王大学）
Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decision-making process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learning-based framework…
Video summarization aims at generating concise video summaries from the lengthy videos, to achieve better user watching experience. Due to the subjectivity, purely supervised methods for video summarization may bring the inherent errors from the annotations. To solve the subjectivity problem, we study the general user summarization process. General users usually watch the whole video, compare interesting clips and select some clips to form a final summary. Inspired by the general user behaviours, we formulate the summarization process as multiple sequential decision-making processes, and propo…
Conventional video summarization approaches based on reinforcement learning have the problem that the reward can only be received after the whole summary is generated. Such kind of reward is sparse and it makes reinforcement learning hard to converge. Another problem is that labelling each frame is tedious and costly, which usually prohibits the construction of large-scale datasets. To solve these problems, we propose a weakly supervised hierarchical reinforcement learning framework, which decomposes the whole task into several subtasks to enhance the summarization quality. This framework cons…
Goal-conditioned reinforcement learning for ultrasound navigation guidance
King’s College London（伦敦国王学院）；Siemens Healthcare Limited（西门子医疗有限公司）；Siemens Healthineers（西门子医疗）；Siemens Healthineers AG（西门子医疗股份公司）；Carol Davila University of Medicine and Pharmacy Bucharest（布加勒斯特卡罗尔·达维拉医药大学）；Fortis Institute of Medical Sciences（富蒂斯医学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健信托基金）
Technical University of Munich（慕尼黑工业大学）；Shanghai Jiao Tong University（上海交通大学）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；rechts der Isar University Hospital（伊萨尔河右岸大学医院）
Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement learning (RL), as an interaction-based learning method, has demonstrated its effectiveness in visual navigating tasks; however, RL is limited in terms of generalization. To address this challenge, we pro…
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China Centre for CIMIM, Manchester University, Manchester, UK School of Artificial Intelligence, Shenzhen University, Shenzhen, China National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China School of Biomedical Engineering；Informatics, Nanjing Medical University, Nanjing, China
Northwestern University（西北大学）；University of California, Riverside（加州大学河滨分校）
For many applications with limited computation, communication, storage and energy resources, there is an imperative need of computer vision methods that could select an informative subset of the input video for efficient processing at or near real time. In the literature, there are two relevant groups of approaches: generating a trailer for a video or fast-forwarding while watching/processing the video. The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users. In the second group, current fas…
US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina
Faculty of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；the Qilu Second Hospital of Shandong University（山东大学第二齐鲁医院）；Innovation School of Artificial Intelligence, Hefei University of Technology（合肥工业大学人工智能创新学院）
University of Central Florida（中佛罗里达大学）；University of Iowa（爱荷华大学）；Tencent AI Lab（腾讯人工智能实验室）
The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity. If videos are lengthy like hours-long egocentric videos, it is necessary to track the temporal structures of the videos and enforce local diversity. The local diversity refers to that the shots selected from a short time duration are diverse but visually similar shots are allowed to co-exist in the summary if they appear far apart in the video. In this paper, we propose a novel probabilistic model, built upon SeqDPP, to d…
End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning
organization= Concordia Institute for Information Systems Engineering, Concordia University , city= Montreal , country= Canada；organization= Department of Mechanical, Industrial；Aerospace Engineering, Concordia University , city= Montreal , country= Canada；organization= Department of Medicine, Laval University , city= Quebec , country= Canada；organization= Department of Electrical, Computer；Software Engineering, Ontario Tech University , city= Oshawa , country= Canada；organization= Department of Computer Science, Khalifa University , city= Abu Dhabi , country= UAE；organization= Artificial Intelligence \& Cyber Systems Research Center, Department of CSM, Lebanese American University , city= Beirut , country= Lebanon；organization= Department of Electrical Engineering；Computer Science, Khalifa University , city= Abu Dhabi , country= UAE
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Virginia Tech（弗吉尼亚理工大学）
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions…
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Sidra Medical and Research Center（西德拉医疗与研究中心）；University Sains Malaysia（马来西亚理科大学）
Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEE…
Imperial College London（伦敦帝国学院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）
This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging. We utilize Neural Style Transfer to create synthetic Computed Tomography (CT) agent gym environments and assess the generalization capabilities of our agents to clinical CT volumes. Our framework does not require any labelled clinical data and integrates easily with several image translation techniques, enabling cross modality applications. Further, we solely condition our agents on 2D slices, breaking grounds for 3D guidance in much more difficult imagi…
Universidade Federal de Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学（UFMG））；Lancaster University（兰卡斯特大学）
The rapid increase in the amount of published visual data and the limited time of users bring the demand for processing untrimmed videos to produce shorter versions that convey the same information. Despite the remarkable progress that has been made by summarization methods, most of them can only select a few frames or skims, which creates visual gaps and breaks the video context. In this paper, we present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos. Our approach can adaptively select frames that are not relevant to convey the informatio…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Universidade Federal de Viçosa（维索萨联邦大学）；Lancaster University（兰卡斯特大学）
The growth of videos in our digital age and the users' limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information. Despite the remarkable progress that summarization methods have made, most of them can only select a few frames or skims, creating visual gaps and breaking the video context. This paper presents a novel weakly-supervised methodology based on a reinforcement learning formulation to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the in…
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程技术研究中心，武汉大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（computing and Data Science学院，南洋理工大学）
PRECISE-AS: Personalized Reinforcement Learning for Efficient Point-of-Care Echocardiography in Aortic Stenosis Diagnosis
Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）；Department of Computer Science, The University of British Columbia（计算机科学系，不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华总医院）
University of Surrey（萨里大学）；University of Edinburgh（爱丁堡大学）
Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability is exploited for saving space or human time by summarizing the essence of input data. In this paper we study a general reinforcement learning based framework for learning to abstract sequential data in a goal-driven way. The ability to define different abstraction goals uniquely…
University College London（伦敦大学学院）；InstaDeep；University College Hospital NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）；Stanford School of Medicine（斯坦福医学院）
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the ad…
