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arXiv 2024年

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering & Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Shandong Computer Science Center China

Weakly supervised semantic segmentation (WSSS) with image-level labels intends to achieve dense tasks without laborious annotations. However, due to the ambiguous contexts and fuzzy regions, the performance of WSSS, especially the stages of generating Class Activation Maps (CAMs) and refining pseudo masks, widely suffers from ambiguity while being barely noticed by previous literature. In this work, we propose UniA, a unified single-staged WSSS framework, to efficiently tackle this issue from the perspective of uncertainty inference and affinity diversification, respectively. When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction. Therefore, we design a more robust feature representation with a probabilistic Gaussian distribution and introduce the uncertainty estimation to avoid the bias. A distribution loss is particularly proposed to supervise the process, which effectively captures the ambiguity and models the complex dependencies among features. When refining pseudo labels, we observe that the affinity from the prevailing refinement methods intends to be similar among ambiguities. To this end, an affinity diversification module is proposed to promote diversity among semantics. A mutual complementing refinement is proposed to initially rectify the ambiguous affinity with multiple inferred pseudo labels. More importantly, a contrastive affinity loss is further designed to diversify the relations among unrelated semantics, which reliably propagates the diversity into the whole feature representations and helps generate better pseudo masks. Extensive experiments are conducted on PASCAL VOC, MS COCO, and medical ACDC datasets, which validate the efficiency of UniA tackling ambiguity and the superiority over recent single-staged or even most multi-staged competitors. Codes will be publicly available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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

SSRN

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

PointMBF: A Multi-scale Bidirectional Fusion Network for Uns...

引用

International Conference on computer Vision (ICCV)

作者： Mingzhi Yuan Kexue Fu Zhihao Li Yucong Meng Manning Wang 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 in Jinan)

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.

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Catalpol ameliorates depressive-like behaviors in CUMS mice via oxidative stress-mediated NLRP3 inflammasome and neuroinflammation

Catalpol ameliorates depressive-like behaviors in CUMS mice ...

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中国解剖学会2021年年会

作者： Wu Haoran Wang Yalin Hu Juntao Department of Anatomy Histology and EmbryologyDepartment of Integrative Medicine and NeurobiologySchool of Basic Medical SciencesInstitutes of Brain ScienceState Key Laboratory of Medical Neurobiology and Collaborative Innovation Center for Brain ScienceShanghai Medical CollegeFudan University.Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai

The purpose of the present study was to investigate whether catalpol exhibited neuro-protective effects in chronic unpredictable mild stress (CUMS) mice through oxidative stress-mediated nucleotide-binding oligomeriza... 详细信息

关键词： Catalpol ameliorates depressive-like behaviors in CUMS mice via oxidative stress-mediated NLRP3 inflammasome and neuroinflammation

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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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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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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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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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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

关键词： Surface measurement

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