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2014年全国压电和声波理论及器件应用研讨会

作者： De-an TA Cheng-cheng LIU Wei-qi WANG Department of Electronic Engineering Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai

Background,Motivation and Objective Osteoporosis is a musculoskeletal disease characterized by a loss of bone mass and a deterioration of trabecular bone *** the size of the aged population continues to grow in modern... 详细信息

关键词： Ultrasound,backscatter parameters,cancellous bone,BMD,correlation

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Prohibitin(PHB)interacts with AKT in mitochondria to coordinately modulate sperm motility

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Asian Journal of Andrology 2020年第6期22卷 583-589页

作者： Xiao-Hui Li Ran-Ran Chai Guo-Wu Chen Ling-Fei Zhang Wen-Jing Tan-Tai Hui-Juan Shi Patricia A Martin-DeLeon Wai-Sum O Hong Chen Department of Anatomy Histology and EmbryologyKey Laboratory of Medical Imaging Computing and Computer Assisted Intervention of ShanghaiSchool of Basic Medical SciencesShanghai Medical CollegeFudan UniversityShanghai 200032China Shanghai Ji'Ai Genetics and IVF Institute Obstetrics and Gynecology HospitalInstitute of Reproduction and DevelopmentFudan UniversityShanghai 200011China Key Lab of Reproduction Regulation of NPFPC-Shanghai Institute of Planned Parenthood Research Fudan University Reproduction and Development InstitutionShanghai 200032China Department of Biological Sciences University of DelawareNewarkDE 19716-2590USA School of Biomedical Sciences The University of Hong KongHong Kong SARChina

Prohibitin(PHB),an evoluti on arily con served mitochondrial inner membra ne protein,is highly expressed in cells that require strong mitoch on drial ***,we dem on strated that the deleti on of Phb in spermatocytes results in impaired mitochondrial *** addition,PHB expression in the mitochondrial sheath of human sperm has a significantly negative correlation with mitochondrial reactive oxygen species levels,but a positive one with mitochondrial membrane potential and sperm *** results suggest that mitochondrial PHB expression plays a role in sperm ***,the mechanism of PHB-mediated regulation of sperm motility remai ns unk ***,we dem on strate for the first time that PHB interacts with protei n kinase B(AKT)and exists in a complex with phospho-PHB(pT258)and phospho-AKT in the mitochondrial sheath of murine sperm,as determined using colocalization and coimmunoprecipitation *** blocking AKT activity using wortmannin(a phosphatidylinositol 3-kinase[PI3K]inhibitor),murine sperm have significantly(P<0.05)decreased levels of phospho-PHB(pT258)and the total and progressive ***,significantly(P<0.05)lower levels of phospho-PI3K P85 subunit a+γ(pY199 and pY46)and phospho-AKT(pS473;pT308)are found in sperm from infertile asthenospermic and oligoasthenospermic men compared with no rmospermic subjects,which suggest a reduced activity of the PI3K/AKT pathway in these in fertile ***,these sperm from infertile subjects also have a significantly(P<0.05)lower level of phospho-PHB(pT258).Collectively,our findings suggest that the interaction of PHB with AKT in the mitochondrial sheath is critical for sperm motility,where PHB phosphorylation(pT258)level and PI3K/AKT activity are key regulatory factors.

关键词： male in fertility prohibitin(PHB) protein kinase B(AKT) sperm motility

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Ultrasound harmonic imaging with reducing speckle noise by an interlaced iterative frequency compounding approach

Ultrasound harmonic imaging with reducing speckle noise by a...

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International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Wei Guo Yuanyuan Wang Jinhua Yu Department of Electronic Engineering Key laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Fudan University Shanghai China

Speckle noise is a phenomenon inherent in any coherent imaging system and decreases the signal-to-noise ratio (SNR), which brings down the imaging quality. Speckle noise reduction is particularly important in the tissue harmonic imaging (THI) since it has the lower energy and the poorer SNR than the fundamental imaging (FI). Speckle can be reduced by frequency compounding (FC), which means incoherently averaging the images with different speckle patterns acquired by changing the center frequency of the pulse. As for the THI, the difference in the emission spectrum doubles when using the FC. In this paper, we propose an interlaced iterative frequency compounding (IIFC) method with the pulse inversion (PI) technique. The new method brings a good speckle suppression performance while the high frame rate is retained. To illustrate the performance of our method, experiments have been conducted on the simulated data. A nonlinear simulation platform based on the full-wave model is used in the harmonic imaging simulation. Results show that our method brings the SNR an improvement of up to 22% in comparison with the single frame THI while maintaining a far better performance in both terms of resolution and contrast than the FI. We also demonstrate that our method for THI retains the high frame rate although using PI.

关键词： Imaging Speckle Harmonic analysis Signal to noise ratio image resolution Ultrasonic imaging Iterative methods

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Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training

arXiv

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arXiv 2024年

作者： Ma, Yingfan Luo, Xiaoyuan Yuan, Mingzhi Chen, Xinrong Wang, Manning The 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

Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example, existing methods often face difficulty in learning hard positive instances. In this paper, we formulate MIL as a semi-supervised instance classification problem, so that all the labeled and unlabeled instances can be fully utilized to train a better classifier. The difficulty in this formulation is that all the labeled instances are negative in MIL, and traditional self-training techniques used in semi-supervised learning tend to degenerate in generating pseudo labels for the unlabeled instances in this scenario. To resolve this problem, we propose a weakly-supervised self-training method, in which we utilize the positive bag labels to construct a global constraint and a local constraint on the pseudo labels to prevent them from degenerating and force the classifier to learn hard positive instances. It is worth noting that easy positive instances are instances are far from the decision boundary in the classification process, while hard positive instances are those close to the decision boundary. Through iterative optimization, the pseudo labels can gradually approach the true labels. Extensive experiments on two MNIST synthetic datasets, five traditional MIL benchmark datasets and two histopathology whole slide image datasets show that our method achieved new SOTA performance on all of them. The code will be publicly available. Impact Statement-MIL is a primary research field within weakly supervised learning and has numerous applications, such as histopathology analysis, video anomaly detection, and drug design. However, current mainstream methods in MIL predominantly rely on bag labels to train the network, consequently overlooking the potential of instance-level supervision information. In our paper, we highlight the

关键词： Self-supervised learning

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Automatic tumor detection in automated 3-D breast ultrasound using local phase information

Automatic tumor detection in automated 3-D breast ultrasound...

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International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Xin Wang Yi Guo Yuanyuan Wang Department of Electronic Engineering Key laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Fudan University Shanghai China

Tumor localization in automated whole breast ultrasound (ABUS) images is an important and challenging task in a computer-aided detection (CAD) system. In this study, a completely automatic tumor detection method using the local phase information was proposed to accurately locate the tumor in ABUS images, thus accelerate the segmenting process. The phase in the max-energy orientation (PMO) image of the preprocessed ABUS image was acquired and binarized as well as the preprocessed image and the composed image obtained from the sum of the preprocessed image and the PMO image. Then morphologic postprocessing was performed to remove non-tumor regions and a region selection algorithm was used to get the tumor region from these three binary 3-D images and generate the volume of interests (VOI). The performance of the method was evaluated using a dataset of 40 cases and the sensitivity achieved 75% and 100% with false positives per volume of 0.25 and 2, respectively. Besides, the average precision rate and average recall rate of the detected VOI were 92.4% and 20.1%. The results proved that the proposed method could deal with various breast tumors and accurately locate the tumor.

关键词： Breast Lesions Gabor filters Three-dimensional displays Ultrasonic imaging Design automation

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A simulation study of the effect of transducer position on photoacoustic spectrum analysis for stochastic microstructure

A simulation study of the effect of transducer position on p...

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International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Jin Wang Chen Zhang Yuanyuan Wang Department of Electronic Engineering Key laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Fudan University Shanghai China

Photoacoustic (PA) imaging is a potential new biomedical imaging technique with combined advantages of ultrasonography's good resolution and high contrast of optical imaging. The spectrum parameters obtained from photoacoustic spectrum analysis (PASA) have been found to have the relationship with tissue microstructure. However, the effects of transducer positions on PASA have not been studied yet. A simplified 2-D simulation model is presented using Monte Carlo method and finite-difference time-domain (FDTD) method to detect PA signals from various transducer positions generated by microspheres with certain radius which have uniformly random positions within the region of interest. PASA is applied and the spectral slope from different distances and angles is extracted and compared with that from microspheres of different radius but at the same positions. It finds that PA spectral slope extracted from some angles show better relationship with dimensions of the microspheres than that from other angles. While changing the detection radius does not have a significant effect on the result.

关键词： Phantoms Transducers Monte Carlo methods Spectral analysis Calibration Fitting

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Ddfp: Data-Dependent Frequency Prompt for Source Free Domain Adaptation of medical image Segmentation

SSRN

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SSRN 2024年

作者： Yin, Siqi Liu, Shaolei Wang, Manning Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

Domain adaptation aims to address model performance degradation problem under domain gap. In the typical setting of unsupervised domain adaptation, labeled data from source domain and unlabeled data from target domain are used to train a target model. However, access to source domain medical data can be restricted due to privacy policies, in which scenario, the research attention has shifted to source free domain adaptation (SFDA) which only requires a trained source domain model and unlabeled target domain data for adaptation. Existing SFDA methods often utilize domain-dependent image style translation and self-supervision techniques to reduce domain gap and train the target domain model. However, the quality of domain-dependent style translated images and pseudo labels still leaves room for improvement. Moreover, training the entire model during adaptation is suboptimal under limited supervision. In this paper, we propose a novel SFDA framework to overcome these limitations. Specifically, to effectively mitigate the impact of domain gap in the initial training phase, we introduce pre-adaptation to derive a pre-adapted model for target model initialization and generate quality enhanced pseudo labels without introducing additional parameters. Furthermore, we propose a data-dependent frequency prompt to better translate target domain images into a source-like style individually. Subsequently, the style-related layers fine-tuning strategy tailored for SFDA is used to train the target model using the prompted target domain images and pseudo labels. Extensive experiments on cross-modality abdominal and cardiac SFDA segmentation tasks demonstrated that our proposed method outperforms the state-of-the-art baselines. Our code is available online. © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification 23

The rise of AI language pathologists: exploring two-level pr...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

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

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP.

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Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

arXiv

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arXiv 2022年

作者： Fu, Kexue Luo, Jiazheng Luo, Xiaoyuan Liu, Shaolei Zhang, Chenxi Wang, Manning The 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

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these 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 a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: https://***/fukexue/RGM. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Boosting Whole Slide image Classification from the Perspectives of Distribution, Correlation and Magnification

Boosting Whole Slide Image Classification from the Perspecti...

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International Conference on computer Vision (ICCV)

作者： Linhao Qu Zhiwei Yang Minghong Duan Yingfan Ma Shuo Wang Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

Bag-based multiple instance learning (MIL) methods have become the mainstream for Whole Slide image (WSI) classification. However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications. In this paper, we propose MILBooster, a powerful dual-scale multi-stage MIL framework to address these issues from the perspectives of distribution, correlation, and magnification. Specifically, to address issue (1), we propose a plug-and-play bag filter that effectively increases the positive instance ratio of positive bags. For issue (2), we propose a novel window-based Transformer architecture called PiceBlock to model the correlation between local and global features of pathology images. For issue (3), we propose a dual-branch architecture to process different magnifications and design an information interaction module called Scale Mixer for efficient information interaction between them. We conducted extensive experiments on four clinical WSI classification tasks using three datasets. MILBooster achieved new state-of-the-art performance on all these tasks. Codes will be available at https://***/miccaiif/MILBooster.

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