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

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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Nonlinear optical response of niobium telluride and its application for demonstrating pulsed fiber lasers

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Journal of Materiomics 2024年第2期10卷 355-365页

作者： Xinxin Shang Yule Zhang Tuo Li Huanian Zhang Xiaofeng Zou S.Wageh Ahmed A.Al-Ghamdi Han Zhang Shuhao Si Dengwang Li Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Shandong Provincial Key Laboratory of Optics and Photonic DeviceSchool of Physics and ElectronicsShandong Normal UniversityJinan250014China Institute of Microscale Optoelectronics College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd.JinanShandongChina School of Physics and Optoelectronic Engineering Shandong University of TechnologyZibo255049China Department of Physics Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Niobium telluride(NbTe_(2)),a kind of few-layer two-dimensional(2D)transition metal dichalcogenides(TMDs)material,has been theoretically predicted with nonlinear absorption properties and excellent optical ***,we experimentally demonstrated an Er-doped fiber(EDF)laser based NbTe_(2)as saturable absorber(SA).Few-layer NbTe_(2)nanosheets were successfully prepared by adopting the commonly used liquid-phase exfoliation(LPE)*** nonlinear optical response of highly stable few-layer NbTe_(2)was investigated through an open-aperture Z-scan laser measurement,the nonlinear absorption coefficient was 2.45×10^(-11)m/*** Q-switched and mode-locked operation centered at 1559 nm were recorded based on NbTe_(2)*** pulse duration was varied from 4.88 ms to 1.75 ms,and the adjustable range of repetition frequency is changed from 44.01 kHz to 64.12 kHz in passively Q-switched ***,a constant repetition rate of 5.33 MHz and pulse width of 2.67 ps were observed in mode-locked *** experimental results fully reveal the nonlinear optical prop-erties of NbTe_(2)used in pulsed fiber lasers and broaden its ultrafast applications in the optics field.

关键词： Niobium telluride Saturable absorber Z-scan Mode-locked pulse Fiber laser

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ISGAN: Unsupervised Domain Adaptation With Improved Symmetric GAN for Cross-Modality Multi-Organ Segmentation

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IEEE Journal of Biomedical and Health Informatics 2024年第6期29卷 3874-3885页

作者： Jiapeng Li Yifan Zhang Lisheng Xu Yudong Yao Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled target domain. The conventional UDA segmentation strategy aims to integrate image generation and segmentation. However, conventional image generation modules only consider information from a single domain (source or target), resulting in visual inconsistencies. The image generation module may also lack anatomical constraints, leading to incorrect pseudo-label generation. To address these issues, we propose an improved symmetric generative adversarial network (ISGAN). Unlike conventional approaches that perform domain adaptation only in the source or target domain, ISGAN adopts a symmetric architecture using two-path domain adaptation to reduce the visual difference. In addition, ISGAN adopts a bidirectional training strategy to optimize the image generation and segmentation modules. The bidirectional training strategy introduces the anatomical constraints into the image generation module, thereby reducing the generation of incorrect pseudo labels. Finally, we validate ISGAN on two cross-modality datasets (the MMWHS cardiac dataset and Abdomen dataset). ISGAN delivers promising segmentation and generalization performance compared with state-of-the-art UDA methods.

关键词： image segmentation Training Biomedical imaging image synthesis Magnetic resonance imaging Computed tomography Feature extraction Generative adversarial networks Generators Diffusion models

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Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI images

Self-Supervised Rotation Learning for 3D Segmentation on Nas...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Changtai Jiang, Ruohui Yin, Shihua Yang, Jinzhu Ban, Xiaojuan University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology Beijing China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Second Affiliated Hospital of Guangxi Medical University Nanning China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350337488

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels. © 2023 IEEE.

关键词： 3D tumor segmentation MRI image nasopharyngeal carcinoma Self-Supervised Learning

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CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior

arXiv

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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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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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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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Rethinking Barely-Supervised Volumetric medical image Segmentation from an Unsupervised Domain Adaptation Perspective

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 Northeastern University Shenyang110819 China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada

This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-annotated labeled set, where each labeled image contains only a single-slice annotation, and 2) an unlabeled set comprising numerous unlabeled volumetric images. State-of-the-art BSS methods employ a registration-based paradigm, which uses inter-slice image registration to propagate single-slice annotations into volumetric pseudo labels, constructing a completely annotated labeled set, to which a semi-supervised segmentation scheme can be applied. However, the paradigm has a critical limitation: the pseudo-labels generated by image registration are unreliable and noisy. Motivated by this, we propose a new perspective: instead of solving BSS within a semi-supervised learning scheme, this work formulates BSS as an unsupervised domain adaptation problem. To this end, we propose a novel BSS framework, Barely-supervised learning via unsupervised domain Adaptation (BvA), as an alternative to the dominant registration paradigm. Specifically, we first design a novel noise-free labeled data construction algorithm (NFC) for slice-to-volume labeled data synthesis. Then, we introduce a frequency and spatial Mix-Up strategy (FSX) to mitigate the domain shifts. Extensive experiments demonstrate that our method provides a promising alternative for BSS. Remarkably, the proposed method, trained on the left atrial segmentation dataset with only one barely-labeled image, achieves a Dice score of 81.20%, outperforming the state-of-the-art by 61.71%. The code is available at https://***/Senyh/BvA. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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MMBDE: A Two-stage Hybrid Feature Selection Method from Microarray Data

MMBDE: A Two-stage Hybrid Feature Selection Method from Micr...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xie, Weidong Chi, Yuhuan Wang, Linjie Yu, Kun Li, Wei Northeastern University Computer Science and Engineering Shenyang China Northeastern University College of Medicine and Bioinformation Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China

ISBN: (纸本)9781665401265

The discovery of diagnostically significant genes from microarray data is essential for disease diagnosis and drug research. However, the difficulty of analyzing microarray data comes from its high dimensionality and small sample size. Feature selection can effectively remove irrelevant and redundant features, reduce data dimensionality, and improve the accuracy of classifiers. This paper proposes a two-stage hybrid feature selection method MMBDE based on the improved min-Redundancy and Max-Relevance (mRMR) and the improved Binary Differential Evolution (BDE) algorithm. The improved mRMR is used to reduce the feature dimensionality at a coarse-scale significantly. In contrast, the improved BDE is used to refine the feature dimensionality at fine-scale further and select the best features. The experimental results show that MMBDE successfully reduces the dimensionality of microarray gene expression data, obtains high classification accuracy, and extracts effective features closely related to diseases from microarray gene expression data. The relevant datasets and codes can be obtained from https://***/xwdshiwo/MMBDE. © 2021 IEEE.

关键词： Feature Selection

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RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

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Multimedia Systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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