检索结果-内蒙古大学图书馆

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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Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction

arXiv

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arXiv 2024年

作者： Meng, Yucong Yang, Zhiwei Duan, Minghong Shi, Yonghong Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China The Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy of Engineering and Technology Fudan University Shanghai200433 China

Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction aims to restore high-quality MRI images from the undersampled k-space. Existing methods typically train deep learning models to map the undersampled data to artifact-free MRI images. However, these studies often overlook the unique properties of k-space and directly apply general networks designed for image processing to k-space recovery, leaving the precise learning of k-space largely underexplored. In this work, we propose a continuous k-space recovery network from a new perspective of implicit neural representation with image domain guidance, which boosts the performance of MRI reconstruction. Specifically, (1) an implicit neural representation based encoder-decoder structure is customized to continuously query unsampled k-values. (2) an image guidance module is designed to mine the image-domain information from the low-quality MRI images to further guide the k-space recovery. (3) a multi-stage training strategy is proposed to recover dense k-space progressively. Extensive experiments conducted on CC359, fastMRI, IXI, and SKM-TEA datasets demonstrate the effectiveness of our method and its superiority over other competitors. Implementation code will be available in https://***/XiaoMengLiLiLi/IGKR. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Low-energy Defibrillation with Multi-electrodes Stimulation: A Simulation Study

Low-energy Defibrillation with Multi-electrodes Stimulation:...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Lian Jin Jianfei Wang Biao Song Xiaomei Wu Zuxiang Fang the Department of Electronic Engineering Fudan University Shanghai China the Electrical Engineering Department Fudan University and the Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai CO 200433 China the Electrical Engineering Department Fudan University Shanghai CO 200433 China

ISBN: (纸本)9781424492695

The objective of this study is to explore the possible ways to reduce defibrillation energy and further reveal the mechanism of electric defibrillation. A bidomain simulation study was performed on a rabbit whole-ventricle electrophysiological model and the feasibility of the defibrillation strategy with multi-electrodes stimulation was verified. Simulation results indicate that the new approach is effective in low-energy defibrillation.

关键词： possible ways further reveal mechanism

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An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution

arXiv

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arXiv 2024年

作者： Duan, Minghong Qu, Linhao Yang, Zhiwei Wang, Manning Zhang, Chenxi Song, Zhijian Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Academy for Engineering and Technology Fudan University Shanghai200433 China

High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image superresolution (SISR) techniques provide an effective way to solve this problem. However, the existing SISR models applied in histopathology images can only work in fixed integer scaling factors, decreasing their applicability. Though methods based on implicit neural representation (INR) have shown promising results in arbitrary-scale super-resolution (SR) of natural images, applying them directly to histopathology images is inadequate because they have unique fine-grained image textures different from natural images. Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale SR of histopathology images to address this challenge. The proposed ISTE contains a feature aggregation branch and a texture learning branch. We employ the feature aggregation branch to enhance the learning of the local details for SR images while utilizing the texture learning branch to enhance the learning of high-frequency texture details. Then, we design a two-stage texture enhancement strategy to fuse the features from the two branches to obtain the SR images. Experiments on publicly available datasets, including TMA, HistoSR, and the TCGA lung cancer datasets, demonstrate that ISTE outperforms existing fixed-scale and arbitrary-scale SR algorithms across various scaling factors. Additionally, extensive experiments have shown that the histopathology images reconstructed by the proposed ISTE are applicable to downstream pathology image analysis tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Image texture

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SP3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation

arXiv

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arXiv 2024年

作者： Li, Shiman Zhao, Jiayue Liu, Shaolei Dai, Xiaokun Zhang, Chenxi Song, Zhijian The School of Basic Medical Science Fudan University 200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention 200032 China The Academy for Engineering and Technology Fudan University 200433 China

Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets. Weakly semi-supervised medical image segmentation is an essential application because it only requires a small amount of scribbles and a large number of unlabeled data to train the model, which greatly reduces the clinician’s effort to fully annotate images. To handle the inadequate supervisory information challenge in weakly semi-supervised segmentation (WSSS), a SuperPixel-Propagated Pseudo-label (SP3) learning method is proposed, using the structural information contained in superpixel for supplemental information. Specifically, the annotation of scribbles is propagated to superpixels and thus obtains a dense annotation for supervised training. Since the quality of pseudo-labels is limited by the low-quality annotation, the beneficial superpixels selected by dynamic thresholding are used to refine pseudo-labels. Furthermore, aiming to alleviate the negative impact of noise in pseudo-label, superpixel-level uncertainty is incorporated to guide the pseudo-label supervision for stable learning. Our method achieves state-of-the-art performance on both tumor and organ segmentation datasets under the WSSS setting, using only 3% of the annotation workload compared to fully supervised methods and attaining approximately 80% Dice score. Additionally, our method outperforms eight weakly and semi-supervised methods under both weakly supervised and semi-supervised settings. Results of extensive experiments validate the effectiveness and annotation efficiency of our weakly semi-supervised segmentation, which can assist clinicians in achieving automated segmentation for organs or tumors quickly and ultimately benefit patients. © 2024, CC BY.

关键词： Superpixels

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

arXiv

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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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PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

arXiv

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arXiv 2023年

作者： Yuan, Mingzhi Fu, Kexue Li, Zhihao Meng, Yucong Wang, Manning Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China Shandong Computer Science Center National Supercomputer Center Jinan China

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous learning-based works commonly focus on supervised registration, which have limitations in practice. Recently, with the advance of inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to achieve unsupervised registration. However, most of existing unsupervised methods follow a cascaded design or fuse RGB-D data in a unidirectional manner, which do not fully exploit the complementary information in the RGB-D data. To leverage the complementary information more effectively, we propose a network implementing multi-scale bidirectional fusion between RGB images and point clouds generated from depth images. By bidirectionally fusing visual and geometric features in multi-scales, more distinctive deep features for correspondence estimation can be obtained, making our registration more accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our method achieves new state-of-the-art performance. Code will be released at https://***/phdymz/PointMBF. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface measurement

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Dual Bidirectional Copy-Paste with Shape Constraint for Semi-Supervised Image Segmentation

Dual Bidirectional Copy-Paste with Shape Constraint for Semi...

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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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Pos-Bert: Point Cloud One-Stage Bert Pre-Training

SSRN

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SSRN 2023年

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

Recently, the pre-training paradigm combining Transformer and masked language modeling in BERT has achieved tremendous success not only in NLP, but also in images and point clouds. However, directly extending BERT from NLP to point clouds requires first training a discrete Variational AutoEncoder (dVAE) as the tokenizer, which results in a complex two-stage process, as in Point-BERT. Inspired by BERT and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point clouds. Specifically, we use the masked patch modeling (MPM) task to perform point cloud pre-training, which aims to recover masked patch information under the supervision of a tokenizer's output. Unlike Point-BERT, whose tokenizer is extra-trained and frozen, we propose a momentum tokenizer which is dynamically updated during training the Transformer. Furthermore, in order to better learn high-level semantic representation, we integrate contrastive learning into the proposed framework to maximize the class token consistency between augmented point cloud pairs. Experiments show that POS-BERT achieves the state-of-the-art performance on linear SVM classification of ModelNet40 with fixed feature extractors, and it exceeds Point-BERT by 3.5%. In addition, POS-BERT has significantly improved many downstream tasks, including fine-tuned classification, few-shot classification and part segmentation. The code and the trained model will be released soon. © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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Differential GR Expression and Translocation in the Hippocampus Mediates Susceptibility vs. Resilience to Chronic Social Defeat Stress

Differential GR Expression and Translocation in the Hippocam...

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中国神经科学学会第十二届全国学术会议

作者： Huimei Wang Qiu-Qin Han Liu Yang Hui-Jie Huang Ya-Lin Wang Rui Yu Jing Wang Xiaorong Chen Adam Pilot Gen-Cheng Wu Qiong Liu Jin Yu Department of Integrative Medicine and Neurobiology State Key Laboratory of Medical Neurobiology School of Basic Medical Sciences Institutes of Brain ScienceBrain Science Collaborative Innovation Center Shanghai Medical College Fudan University Department of Anatomy Histology and EmbryologySchool of Basic Medical Sciences Shanghai Medical College Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai

While social stress exposure is a common risk factor for affective disorders, most individuals exposed to it can maintain normal physical and psychological functioning. However, factors that determine

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