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2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Ri, Jonghyok Shang, Longzhong Pang, Na Xu, Lisheng Ji, Ning Yue, Xiangji Kim, Insong Zheng, Dingchang College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Mechanical Engineering and Automation Northeastern University Shenyang110169 China Postdoctoral Mobile Station for Basic Medicine Hebei Medical University Hebei Shijiazhuang050017 China Key Laboratory of Medical Image Computing Ministry of Education Shenyang110169 China Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang110169 China School of Metallurgy Northeastern University Shenyang110819 China Research Centre of Intelligent Healthcare Coventry University Coventry United Kingdom

ISBN: (纸本)9781510682825

For the implementation of sonothrombolysis, the acoustic pressure inside the blood vessel should be revealed according to the ultrasound burst and biological tissue conditions. The objective of this study is to measure the magnitude of the acoustic pressure inside blood vessel exposed to high-intensity focused ultrasound (HIFU), and to reveal its changing characteristics according to the ultrasound parameters (power and frequency) and tissue configurations. The tissue mimicking phantom with HIFU exposure was modeled to simulate the acoustic pressure. The results showed that for a biological tissue system composed of skin, fat, muscle, and blood, the peak pressure at the focus with blood zone increased as the insonation frequency increased (0.5-2 MHz). Pressure attenuation with respect to blood vessel depth (10-30 mm) intensified according to increment of HIFU power and frequency. Greater attenuation was observed when the frequency surpassed 1.1 MHz, varying with skin (1-5 mm) and fat tissue (2-7 mm) thicknesses. The results suggest that at frequencies below 1.1 MHz, identical HIFU power can be utilized for different individuals and lesions, thereby achieving similar outcomes in clinical treatment. © 2024 SPIE.

关键词： Disease control

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Dynamic Neural Radiance Field From Defocused Monocular Video

arXiv

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arXiv 2024年

作者： Luo, Xianrui Sun, Huiqiang Peng, Juewen Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore

Dynamic Neural Radiance Field (NeRF) from monocular videos has recently been explored for space-time novel view synthesis and achieved excellent results. However, defocus blur caused by depth variation often occurs in video capture, compromising the quality of dynamic reconstruction because the lack of sharp details interferes with modeling temporal consistency between input views. To tackle this issue, we propose D2RF, the first dynamic NeRF method designed to restore sharp novel views from defocused monocular videos. We introduce layered Depth-of-Field (DoF) volume rendering to model the defocus blur and reconstruct a sharp NeRF supervised by defocused views. The blur model is inspired by the connection between DoF rendering and volume rendering. The opacity in volume rendering aligns with the layer visibility in DoF rendering. To execute the blurring, we modify the layered blur kernel to the ray-based kernel and employ an optimized sparse kernel to gather the input rays efficiently and render the optimized rays with our layered DoF volume rendering. We synthesize a dataset with defocused dynamic scenes for our task, and extensive experiments on our dataset show that our method outperforms existing approaches in synthesizing all-in-focus novel views from defocus blur while maintaining spatial-temporal consistency in the scene. Copyright © 2024, The Authors. All rights reserved.

关键词： Volume rendering

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An Unsupervised Domain Adaptation Model Based on Multi-Level Joint Alignment for Multi-Modal Cardiac image Segmentation

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Procedia Computer Science 2023年 226卷 106-112页

作者： Jiapeng Li Yimou Lv Lisheng Xu Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Unsupervised Domain Adaptation has greatly boosted the performance of multi-modal medical segmentation when there are only source domain labels and no labels in the target domain. Many previous work relies on Convolutional Neural Networks for distribution or instance alignment, however, the receptive field of CNN makes it overly concerned with texture and local semantic information of images, while losing the global style information and semantic information and lacking relationships between local and global semantics. In order to address these issues, we propose a two-stage, multi-level framework for unsupervised domain adaptation, which consists of an image translation network and a Transformer-based domain adaptation segmentation network, jointly aligning the data distribution in the source and target domains from the image, feature and output level through adversarial learning. Experimental results indicate that our method can achieve satisfactory results and outperform other state-of-the-art medical image segmentation methods.

关键词： Unsupervised domain adaptation Cardiac image segmentation Adversarial learning Multi-modal segmentation

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Adaptive Mix for Semi-Supervised medical image Segmentation

arXiv

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arXiv 2024年

作者： Shen, Zhiqiang Cao, Peng Su, Junming Yang, Jinzhu Zaiane, Osmar R. School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules, such as replacing low-confidence patches with high-confidence ones. The former lacks control over the perturbation degree, leading to overfitting on randomly perturbed samples, while the latter tends to generate images with trivial perturbations, both of which limit the effectiveness of consistency learning. This paper aims to answer the following question: How can image mix-up perturbation be adaptively performed during training? To this end, we propose an Adaptive Mix algorithm (AdaMix) for image mix-up in a self-paced learning manner. Given that, in general, a model's performance gradually improves during training, AdaMix is equipped with a self-paced curriculum that, in the initial training stage, provides relatively simple perturbed samples and then gradually increases the difficulty of perturbed images by adaptively controlling the perturbation degree based on the model's learning state estimated by a self-paced regularize. We develop three frameworks with our AdaMix, i.e., AdaMix-ST, AdaMix-MT, and AdaMix-CT, for semi-supervised medical image segmentation. Extensive experiments on three public datasets, including both 2D and 3D modalities, show that the proposed frameworks are capable of achieving superior performance. For example, compared with the state-of-the-art, AdaMix-CT achieves relative improvements of 2.62% in Dice similarity coefficient and 48.25% in average surface distance on the ACDC dataset with 10% labeled data. The results demonstrate that mix-up operations with dynamically adjusted perturbation strength based on the segmentation model's state can significantly enhance the effectiveness of consistency regularization. The code will be released at https://***/Senyh/AdaMix. Copyright © 2024,

关键词： Self-supervised learning

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Incorporating dynamic attention gating mechanism and pre-trained embedding for Chinese clinical named entity recognition

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Procedia Computer Science 2023年 226卷 113-119页

作者： Zhenming Qiu Anyu Pan Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Clinical Named Entity Recognition (CNER) serves as a cornerstone in clinical research and healthcare informatics, with its primary goal being the discernment and categorization of clinical terminologies present within Electronic medical Records (EMRs). While deep learning has significantly improved CNER's performance, these models still face challenges in identifying rare or unseen entities. Our study unveils an innovative DAGM-BiLSTM-CRF model that seamlessly integrates data-driven deep learning strategies with human knowledge to surmount this limitation. The distinctive features of this model lie in its integration of a pre-trained character embedding layer, adept at encapsulating nuanced semantic variations at the granularity of individual characters. Furthermore, it incorporates a bidirectional Long Short-Term Memory (BiLSTM) layer, proficient in deriving contextually relevant information by traversing the sequence from both endpoints. The most noteworthy aspect of our model is the inclusion of a Dynamic Attention Gating Mechanism (DAGM) that dynamically recalibrates attention weights based on the traits of the input sequence and a Conditional Random Fields (CRF) layer that considers interdependencies among output labels within a sequence. The evaluation was carried out on the CCKS-2017 CNER benchmark dataset to underscore the superiority of our model over existing cutting-edge methods, thereby validating its proficiency in deciphering the intricacies of Chinese electronic medical records.

关键词： Clinical named entity recognition deep learning dynamic attention gating mechanism

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CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior

arXiv

引用

arXiv 2023年

作者： Gao, Pan Han, Donghong Zhou, Rui Zhang, Xuejiao Wang, Zikun School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Swinburne University of Technology Australia

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors’ emotions. The reason is that considering both interlocutors’ emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation. © 2023, CC BY.

关键词： Knowledge management

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Transwnet: Integrating Transformers into CNNS via Row and Column Attention for Abdominal Multi-Organ Segmentation 48

Transwnet: Integrating Transformers into CNNS via Row and Co...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xie, Yazhen Huang, Yanglin Zhang, Yuan Li, Xuanya Ye, Xiongjun Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Chinese Academy of Medical Sciences and Peking Union Medical College National Cancer Center Cancer Hospital Beijing100021 China

ISBN: (纸本)9781728163277

Learning how to model global relationships and extract local details is crucial in improving the performance of multi-organ segmentation. Most existing U-shaped structure methods use feature fusion to address these two challenges, but still lack the ability to balance capturing global relationships and local details. To address these issues, we propose a novel multi-organ segmentation framework called TransWnet to mine global relationships and local details from both intra-and inter-scale perspectives. To achieve this, we innovatively design a Row and Column Swin Transformer (RCST) module that can efficiently capture global contextual features and construct local information. Specifically, we design a parallel structure of Row and Column Attention to model the global relationships of multi-scale encoded features, and further mine local information from the global relationships through a local window mechanism. Extensive experiments on the Synapse dataset show that our method outperforms state-of-The-Art approaches and achieves accurate segmentation of abdominal multi-organs. © 2023 IEEE.

关键词： Computerized tomography

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Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

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IEEE Transactions on medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

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Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

引用

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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