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IEEE International Symposium on Biomedical Imaging

作者： Bowei Shen Xiaoquan Huang Yonghong Shi Shiyao Chen School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Gastroenterology and Hepatology Zhongshan Hospital Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Semi-supervised learning utilizes unlabeled data and a small amount of pixel-level labeled data to achieve image semantic segmentation. However, its performance largely depends on the semantic learning of the boundary details of the object in the image, as the uncertainty of the boundary region can reduce the segmentation accuracy. To this end, this paper proposes a novel semi-supervised learning method based on the student-teacher model, which is constrained by the dual bidirectional copy-paste and the signed distance map of the object shape. The method is carried out through two stages: fully supervised pre-training and semi-supervised re-training. The former uses labeled data to train and learn the initial student model, and its parameters are used to initialize and update the latter’s teacher model, which is used to segment unlabeled data to obtain its pseudo labels. At each stage, the dual bidirectional copy-paste between ‘labeled data’ enhances the data, while the similar copy-paste performed on the corresponding ‘label’ supervises the student model. Copy-paste allows the network to focus on the entire object, while the signed distance map on the label data helps to learn the complex shape changes of the object boundary. Especially in the re-training stage, the above calculation mainly occurs between labeled data and unlabeled data with 'pseudo label', which effectively bridges the empirical distribution mismatch between labeled data and unlabeled data, enabling the network to learn a more generalized data distribution. Our method was validated on two datasets, LA Heart and ACDC. Compared with other semi-supervised learning methods, our method outperforms previous works at 5% and 10% labeled data volumes, achieving the SOTA results.

关键词： Bridges Heart Uncertainty Shape Biological system modeling Semantic segmentation Semantics

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Rethinking Multiple Instance Learning for Whole Slide image Classification: A Good Instance Classifier is All You Need

arXiv

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arXiv 2023年

作者： Qu, Linhao Ma, Yingfan Luo, Xiaoyuan Guo, Qinhao Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Department of Gynecologic Oncology Fudan University Shanghai Cancer Center 270 Dong-An Road Shanghai200032 China

Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggregate instance features into a bag feature through attention mechanisms and then train a bag classifier, where the attention scores can be used for instance-level classification. However, the pseudo instance labels constructed by the former usually contain a lot of noise, and the attention scores constructed by the latter are not accurate enough, both of which affect their performance. In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks. To this end, we propose an instance-level weakly supervised contrastive learning algorithm for the first time under the MIL setting to effectively learn instance feature representation. We also propose an accurate pseudo label generation method through prototype learning. We then develop a joint training strategy for weakly supervised contrastive learning, prototype learning, and instance classifier training. Extensive experiments and visualizations on four datasets demonstrate the powerful performance of our method. Codes are available at https://***/miccaiif/INS. Copyright © 2023, The Authors. All rights reserved.

关键词： image classification

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： image registration

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PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration

arXiv

引用

arXiv 2022年

作者： Yuan, Mingzhi Li, Zhihao Jin, Qiuye Chen, Xinrong Wang, Manning Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Academy for Engineering and Technology Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud. Solving this problem is challenging since inlier correspondences of one instance constitute outliers of all the other instances. Existing methods often rely on time-consuming hypothesis sampling or features leveraging spatial consistency, resulting in limited performance. In this paper, we propose PointCLM, a contrastive learning-based framework for mutli-instance point cloud registration. We first utilize contrastive learning to learn well-distributed deep representations for the input putative correspondences. Then based on these representations, we propose a outlier pruning strategy and a clustering strategy to efficiently remove outliers and assign the remaining correspondences to correct instances. Our method outperforms the state-of-the-art methods on both synthetic and real datasets by a large margin. The code will be made publicly available at http://***/phdymz/PointCLM. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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A Novel Interpretable Feature Set Optimization Method in Blood Pressure Estimation Using Photoplethysmography Signals

SSRN

引用

SSRN 2023年

作者： Liu, Jian Hu, ShuaiCong Xiao, Zhijun Hu, Qihan Wang, Daomiao Yang, CuiWei Centre for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai 200093 China School of Instrument Science and Engineering Southeast University Nanjing210096 China

Blood pressure (BP) estimation based on photoplethysmography (PPG) signals enables continuous and comfortable BP measurement, which is important for the clinical management of hypertension. The purpose of this study is to propose a novel and interpretable feature optimization method to improve the performance of the PPG-based model for BP estimation. The PPG signals of 152 subjects were selected from a public database. Feature detection was performed on the signals after FIR band-pass filtering. A total of 172 features extracted from ten feature dimensions were used to construct a feature set, including features from the raw PPG signals, PPG derivative signals, and statistical information. Light Gradient Boosting Machine (LightGBM) was used as this work's prediction model. To further improve the performance of the LightGBM model, Shapley Additive Explanations (SHAP) were utilized for feature optimization to achieve the purpose. The number of features corresponding to the lowest model error was defined as the optimal feature set, and this was utilized for training the model to obtain BP estimation. Compared to the validated BP, the mean and standard deviation (SD) of the estimation errors for systolic blood pressure (SBP) and diastolic blood pressure (DBP) were -0.73 ± 6.50 mmHg and 0.37 ± 3.83 mmHg, respectively. According to the British Hypertension Society (BHS) criteria, SBP and DBP are within the range of B and A grades, respectively. We propose a novel feature optimization method to reduce the feature dimension for BP estimation. The algorithm can effectively prevent overfitting and improve the model's performance. © 2023, The Authors. All rights reserved.

关键词： Photoplethysmography

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Joint Hand-Object 3D Reconstruction From Monocular image Based On Fused Visual Cues And Pose Prior

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Yawen Zhang, Xinkang Chen, Xinrong Fudan University Academy for Engineering and Technology 200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention 200032 China

Joint reconstruction of hands and manipulated objects from a monocular image is important perception technique which has recently achieved impressive progress. Compared to reconstructing hands and objects from temporal sequences, hand and object reconstruction from a single monocular image does not require continuous image frames and can be performed with just one camera, which is applicable to a wider range of scenarios. In existing methods, signed distance field is widely used due to its excellent geometric representation ability. However, visual cues and pose prior information are not fully explored in signed distance field estimation. Therefore, this paper proposes a new hand-object reconstruction framework to fuse implicit representations with more representative prior information, in which other prior information and visual cues are fully explored. First, when signed distance value of a 3D point is queried, both global features and local features from 2D image plane transformed from the 3D space are integrated. Second, the anchor-based mechanism is introduced into the hand keypoints prediction, which provides a more accurate pose prior. In the comparison with the state-of-the-art model, the experiment results on common benchmarks demonstrated that the proposed method had good performance and achieved a shape error about 0.106 cm2 on Obman and 0.412 cm2 on DexYCB. Especially on the Obman dataset, the hand chamfer distance CDh reduces 5.4%, the object chamfer distance CDo reduces 27.1%, the hand keypoints error Eh reduces 5.7% and the object center error Eo reduces 9.0%. © 1975-2011 IEEE.

关键词： Surface reconstruction

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Intracardiac Electrogram Signals key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data medical services Myocardium Feature extraction Pattern recognition Testing

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A Learnable self-supervised task for unsupervised domain adaptation on point clouds

arXiv

引用

arXiv 2021年

作者： Luo, Xiaoyuan Liu, Shaolei Fu, Kexue Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

Deep neural networks have achieved promising performance in supervised point cloud applications, but manual annotation is extremely expensive and time-consuming in supervised learning schemes. Unsupervised domain adaptation (UDA) addresses this problem by training a model with only labeled data in the source domain but making the model generalize well in the target domain. Existing studies show that self-supervised learning using both source and target domain data can help improve the adaptability of trained models, but they all rely on hand-crafted designs of the self-supervised tasks. In this paper, we propose a learnable self-supervised task and integrate it into a self-supervision-based point cloud UDA architecture. Specifically, we propose a learnable nonlinear transformation that transforms a part of a point cloud to generate abundant and complicated point clouds while retaining the original semantic information, and the proposed self-supervised task is to reconstruct the original point cloud from the transformed ones. In the UDA architecture, an encoder is shared between the networks for the self-supervised task and the main task of point cloud classification or segmentation, so that the encoder can be trained to extract features suitable for both the source and the target domain data. Experiments on PointDA-10 and PointSegDA datasets show that the proposed method achieves new state-of-the-art performance on both classification and segmentation tasks of point cloud UDA. Code will be made publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient image Analysis

arXiv

引用

arXiv 2023年

作者： Jiao, Jing Zhou, Jin Li, Xiaokang Xia, Menghua Huang, Yi Huang, Lihong Wang, Na Zhang, Xiaofan Zhou, Shichong Wang, Yuanyuan Guo, Yi Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai China Fudan University Shanghai Cancer Center Shanghai China Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States SenseTime Research Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Robust point cloud registration framework based on deep graph matching

arXiv

引用

arXiv 2021年

作者： Fu, Kexue Liu, Shaolei Luo, Xiaoyuan Wang, Manning Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

3D point cloud registration is a fundamental problem in computer vision and robotics. There has been extensive research in this area, but existing methods meet great challenges in situations with a large proportion of outliers and time constraints, but without good transformation initialization. Recently, a series of learning-based algorithms have been introduced and show advantages in speed. Many of them are based on correspondences between the two point clouds, so they do not rely on transformation initialization. However, these learning-based methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by singular value decomposition. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on registering clean, noisy, partial-to-partial and unseen category point clouds show that the proposed method achieves state-of-the-art performance. The code will be made publicly available at https://***/fukexue/RGM. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface measurement

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