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2015 World Conference on Control, Electronics and Electrical Engineering(WCEE 2015)

作者： Yifeng Wang Xiaomei Wu Jianguo Yu Department of Electronic Engineering Fudan University Digital Medical Research Center of Fudan University Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI)

In a rotating coil based magnetic tracking system,so long as the rotating coil has seized the direction of the magnetic sensor,geometric algorithm can compute the position and orientation of the target object without tedious iterative ***,the rotation process of the coil is still time *** the coil will be rotated in a plane,the measured magnetic field density should change as a sine/cosine-like *** on this,we proposes a searching strategy in which field densities are measured along four sparse even spaced directions to lock an initial searching range,and then the searching range is continuously halved around the measured maximum field density until a required tracking precision is *** results show that the geometric tracking algorithm with this global searching strategy can track the target object with higher efficiency and higher precision than the algorithm without this searching ***,in the condition of low field noises,the optimized non-iterative algorithm with global searching strategy has better precision than the traditional iterative algorithms as well.

关键词： adaptive algorithm,binary search,global optimization,local optimization,magnetic tracking,non-iterative algorithm.

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification 38

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fu, Kexue Luo, Xiaoyuan Qu, Linhao Wang, Shuo Xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Qilu University of Technology Shandong Academy of Sciences Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST. © 2024 Neural information processing systems foundation. All rights reserved.

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Segmentation of arteriovenous malformations nidus and vessel in digital subtraction angiography images based on an iterative thresholding method

Segmentation of arteriovenous malformations nidus and vessel...

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International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Yuxi Lian Yuanyuan Wang Jinhua Yu Yi Guo Liang Chen Department of Electronic Engineering Fudan University Shanghai China Key laboratory of Medical Imaging Computing and-Computer Assisted Intervention of Shanghai China Department of Neurosurgery Fudan University Shanghai China

Digital subtraction angiography (DSA) plays an important role in the diagnosis and therapy of vascular diseases. Segmentation of nidus and vessel in DSA images is an essential step in the diagnosis of arteriovenous malformations (AVM). In this paper, a novel segmentation method based on the global and iterative local thresholding is proposed to segment the nidus and vessel in DSA images. Firstly, the original image is divided into proper subimages. For each subimage, Ostu's method is primarily used and pixels are classified into two groups by the threshold. Then, according to the variance of the subimage intensities, the mean or median values of two groups are calculated to sort the pixels into three classes. These three classes represent the dark AVM and vessel, the bright background and undetermined regions in the original DSA image. The first two classes are determined directly and will not be processed further. The undetermined regions are processed in the next iteration to segment tiny vessels until the thresholds between two iterations are less than a preset one. Finally, all classes are combined to create the segmentation result. We test this method on DSA images of the AVM. Experimental results show that the proposed method performs better than the other state-of-the-art methods in the segmentation of DSA images. The proposed method can identify fine and tiny vessel structures, as well as distinguish large AVM nidus in one framework.

关键词： Image segmentation Iterative methods Angiography Diseases Blood vessels Hospitals

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Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

arXiv

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arXiv 2024年

作者： Hu, Qihan Wang, Daomiao Wu, Hong Liu, Jian Yang, Cuiwei Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200093 China

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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Organ at risk segmentation in head and neck CT images by using a two-stage segmentation framework based on 3D U-Net

arXiv

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arXiv 2018年

作者： Wang, Yueyue Zhao, Liang Song, Zhijian Wang, Manning School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Digital Medical Research Center Fudan University Shanghai200032 China

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one. Copyright © 2018, The Authors. All rights reserved.

关键词： computerized tomography

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Boosting Point-BERT by Multi-choice Tokens

arXiv

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arXiv 2022年

作者： Fu, Kexue Yuan, Mingzhi Wang, Manning Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai China

Masked language modeling (MLM) has become one of the most successful self-supervised pre-training task. Inspired by its success, Point-BERT, as a pioneer work in point cloud, proposed masked point modeling (MPM) to pre-train point transformer on large scale unanotated dataset. Despite its great performance, we find the inherent difference between language and point cloud tends to cause ambiguous tokenization for point cloud. For point cloud, there doesn’t exist a gold standard for point cloud tokenization. Point-BERT use a discrete Variational AutoEncoder (dVAE) as tokenizer, but it might generate different token ids for semantically-similar patches and generate the same token ids for semantically-dissimilar patches. To tackle above problem, we propose our McP-BERT, a pre-training framework with multi-choice tokens. Specifically, we ease the previous single-choice constraint on patch token ids in Point-BERT, and provide multi-choice token ids for each patch as supervision. Moreover, we utilitze the high-level semantics learned by transformer to further refine our supervision signals. Extensive experiments on point cloud classification, few-shot classification and part segmentation tasks demonstrate the superiority of our method, e.g., the pre-trained transformer achieves 94.1% accuracy on ModelNet40, 84.28% accuracy on the hardest setting of ScanObjectNN and new state-of-the-art performance on few-shot learning. We also demonstrate that our method not only improves the performance of Point-BERT on all downstream tasks, but also incurs almost no extra computational overhead. The code will be released in https://***/fukexue/McP-BERT. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

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RNA splicing alterations in lung cancer pathogenesis and therapy

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Cancer Pathogenesis and Therapy 2023年第4期1卷 272-283页

作者： Yueren Yan Yunpeng Ren Yufang Bao Yongbo Wang Department of Thoracic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Department of Cellular and Genetic Medicine Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted InterventionSchool of Basic Medical SciencesFudan UniversityShanghai 200032China

RNA splicing alterations are widespread and play critical roles in cancer pathogenesis and *** cancer is highly heterogeneous and causes the most cancer-related deaths ***-scale multi-omics studies have not only characterized the mutational landscapes but also discovered a plethora of transcriptional and post-transcriptional changes in lung *** resources have greatly facilitated the development of new diagnostic markers and therapeutic options over the past two ***,altered RNA splicing has emerged as an important molecular feature and therapeutic target of lung *** this review,we provide a brief overview of splicing dysregulation in lung cancer and summarize the recent progress on key splicing events and splicing factors that contribute to lung cancer ***,we describe the general strategies targeting splicing alterations in lung cancer and highlight the potential of combining splicing modulation with currently approved therapies to combat this deadly *** review provides new mechanistic and therapeutic insights into splicing dysregulation in cancer.

关键词： RNA splicing Splicing factors Splicing alterations Lung cancer

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xinhan Di Xiaokun Dai Xinkang Zhang Xinrong Chen Deepearthgo Academy for Engineering&Technology Fudan Universiry Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users’ virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results.

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A Five-Channel Weighted Real-Time Algorithm for High-Density Electrodes Spike Sorting

A Five-Channel Weighted Real-Time Algorithm for High-Density...

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

作者： Jiaxin He Chongyuan Ren Yu Ma Yizhou Jiang Yajie Qin School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai Shanghai China Shanghai Engineering Research Center of Assistive Devices Shanghai China

With the application of high-density neural probes, a neuron can be detected by multiple adjacent probes, and the traditional single-channel spike sorting is no longer suitable. In this paper, we propose a five-channel weighted real-time spike sorting algorithm based on template-matching to process neural signals recorded by high-density probes. This work uses the signals of the center channel and the adjacent four channels to form a five-channel template by weighting, and employs a modified OSort algorithm with unsupervised learning to update the template. We implemented automatic online spike sorting, and tested it with both ground truth recordings and simulated datasets. The experiments show that our algorithm utilizing the information of adjacent channels has a higher sorting accuracy than traditional single-channel spike sorting. The average sorting accuracy reaches 89%, compared to 78% for single-channel.

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A Novel Method to Detect Multiple Arrhythmias Based on Time-Frequency Analysis and Convolutional Neural Networks

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IEEE ACCESS 2019年 7卷 170820-170830页

作者： Wu, Ziqian Lan, Tianjie Yang, Cuiwei Nie, Zhenning Department of Electronic Engineering Fudan University Shanghai 200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai Engineering Research Center of Assistive Devices Fudan University Shanghai 200093 China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University Shanghai 200032 China

Electrocardiogram (ECG) is an efficient and commonly used tool for detecting arrhythmias. With the development of dynamic ECG monitoring, an effective and simple algorithm is needed to deal with large quantities of ECG data. In this study, we proposed a method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks. For a short-time (10 s) single-lead ECG signal, the time-frequency distribution matrix of the signal was first obtained using a time-frequency transform method, and then a convolutional neural network was used to discriminate the rhythm of the signal. ECG data in multiple databases were used and were divided into 12 classes. Finally, the performance of three kinds of time-frequency transform methods are evaluated, including short-time Fourier transform (STFT), continuous wavelet transform (CWT), and pseudo Wigner-Ville distribution (PWVD). The best result was obtained by STFT, with an accuracy of 96.65%, an average sensitivity of 96.47%, an average specificity of 99.68%, and an average F-1 score of 96.27%, respectively. Especially, the area under curve (AUC) value is 0.9987. The proposed method in this work may be efficient and valuable to detect multiple arrhythmias for dynamic ECG monitoring.

关键词： Electrocardiography Time-frequency analysis Rhythm Databases Transforms Feature extraction Heart beat Arrhythmia detection convolutional neural networks ECG time-frequency analysis

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