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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Sun, Chen Li, Chunpu Huo, Hongye Zhong, Gaoyan Liu, Sen Wang, Aiguo Deng, Xintao Yang, Cuiwei Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Department of Cardiology Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Atrial fibrillation (AF) is the most common chronic cardiovascular disease. As the disease progresses, AF could have various impact on the quality of life and the health of patients. The scientific management of AF depends on the development of targeted therapeutic measures according to the disease course. In view of the noninvasiveness and convenience of electrocardiogram (ECG), this study attempted to assess the course of AF based on standard 12-lead ECG. Therefore, based on transfer learning, we used MobileNetV2 to achieve automatic grading of AF treatment. By taking 12-lead ECG images of patients with mild and severe AF as network input, the pre-trained model was retrained to recognize different courses. The results show that our proposed method exhibits excellent performance with accuracy of 84.1 %, precision of 86.5%, recall of 83.1%, specificity of 85.3%, and F1-score of 84.8%. Our study provides a feasible way of assessing the course of AF and is of great significance to promote the graded diagnosis and treatment of AF. © 2022 IEEE.

关键词： Electrocardiograms

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IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

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Journal of Genetics and Genomics 2024年第4期51卷 443-453页

作者： Shuyu Liang Sicheng Xu Shichong Zhou Cai Chang Zhiming Shao Yuanyuan Wang Sheng Chen Yunxia Huang Yi Guo Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai Shanghai 200032China Shanghai Key Laboratory of Metabolic Remodeling and Health Institute of Metabolism and Integrative BiologyFudan UniversityShanghai 200433China Department of Ultrasound Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China Department of Breast Surgery Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai 200032China

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular subtypes is crucial for understanding disease mechanisms and providing personalized *** present a well-designed radiogenomic framework image–gene–gene set(IMAGGS),which detects multi-way associations in BC subtypes by integrating radiomic and genomic *** dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation *** better characterize tumor traits,12 multi-angle US images are fused using two distinct ***,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous *** radiomic and genomic features are screened using these *** addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC *** further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and *** together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.

关键词： IMAGGS Radiogenomic framework "lmage-gene-geneset"associations Molecular subtypes Breast cancer

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Research of an Electromagnetic Tracking Method Based on Two Electrically-controlled Magnetic Sources

Research of an Electromagnetic Tracking Method Based on Two ...

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2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Min Sha Yifeng Wang Ning Ding Xiaomei Wu Zuxiang Fang Department of Electronic Engineering Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (纸本)9781479919819

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic *** source consists of three mutual orthogonal *** method can skillfully determine the rotation angles of the maximum magnetic flux density vector(MMFD V) generated by a magnetic source according to the Biot-Savart *** the position of the sensor can be calculated with a noniterative geometrical algorithm in real *** results demonstrate a good performance of this method.

关键词： electromagnetic tracking MMFDV fast determination algorithm finite element analysis

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Intelligent Detection of Hypokalemia Based on 12-Lead ECG Using Two-stream Deep Learning Model 15

Intelligent Detection of Hypokalemia Based on 12-Lead ECG Us...

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15th International Congress on image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Wang, Yueyi Zhong, Gaoyan Wang, Ya'nan Zhu, Qiang Xie, Jiayong Deng, Xintao Wang, Aiguo Yang, Cuiwei School of Information Science and Technology Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Hypokalemia is one of the most common electrolyte disorders in clinic. The detection of hypokalemia mainly depends on the detection of serum potassium concentration. Previous studies have shown that with the decrease of serum potassium ion concentration, ECG will show corresponding characteristics. In this paper, 12-lead ECG is used for intelligent detection of hypokalemia. After six artificial features based on ECG are extracted, a two-stream deep learning model is trained by using these features and 12-lead ECG to detect hypokalemia. The AVC of the two-stream model on the verification set is 0.84, and the AVC on the test set is 0.82. After taking the best working point, on the verification set, the sensitivity is 81.45%, the specificity is 74.21 %, and the recognition accuracy is 77.82%, while on the test set, the sensitivity is 77.54%, the specificity is 74.28%, and the recognition accuracy is 75.91 %. The results show that these time-domain features can significantly improve the recognition accuracy of hypokalemia. © 2022 IEEE.

关键词： Electrocardiograms

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3D Clothed Human Reconstruction From One In-the-Wild RGB image

3D Clothed Human Reconstruction From One In-the-Wild RGB Ima...

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IEEE International Conference on image Processing

作者： Liangjing Shao Benshuang Chen Xinrong Chen Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In recent years, much achievement have been made in the field of 3D clothed human reconstruction. However, most of researches performed not well for reconstruction from in-the-wild images due to the domain gap between the synthetic images of training datasets and the in-the-wild images. In this study, a modular model, including clothes encoder, body encoder and cloth generator, is proposed to perform 3D clothed human reconstruction from one single-view in-the-wild RGB image. In particular, we introduce the adaptive aggregation of convolution and multi-head attention into the cloth encoder and apply the adjustment of the segmentation at the preprocessing stage. According to experiments on MSCOCO and 3DPW datasets, the proposed method achieves state-of-the-art performance on 3D clothed human reconstruction from in-the-wild images compared with previous works.

关键词： Training Solid modeling image segmentation Adaptation models Three-dimensional displays Convolution Data preprocessing

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Exosome-Transmitted miR-224-5p Promotes Colorectal Cancer Cell Proliferation via Targeting ULK2 in p53-Dependent Manner

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Biomedical and Environmental Sciences 2024年第1期37卷 71-84页

作者： YANG Le Mei ZHENG Qi LIU Xiao Jia LI Xian Xian Veronica Lim CHEN Qi ZHAO Zhong Hua WANG Shu Yang Department of Pathology School of Basic MedicineFudan UniversityShanghai 200032China Department of Pathology Dunedin School of MedicineUniversity of OtagoDunedin 9054New Zealand Department of Pathology Huashan HospitalFudan UniversityShanghai 200040China Shanghai Cancer Institute Renji HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai 200127China Teladoc Health Inc Shanghai 200335China Shanghai Medical College Fudan UniversityShanghai 200032China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai 200032China

Objective To investigate the role and molecular mechanism of exosomal miR-224-5p in colorectal cancer(CRC).Methods The miR-224-5p expression in CRC patient tissues and cell-derived exosomes was measured by laser capture microdissection and qRT-PCR,***-luciferase reporter gene assay was used to determine the target gene of *** protein expressions of p53 and unc-51 like kinase 2(ULK2)in CRC cells were detected by western *** cytometry was used to detect cell cycle and *** proliferation was measured by CCK8 and EdU *** The miR-224-5p expression was upregulated in CRC tissues and increased progressively with the rise of CRC *** cells secreted extracellular miR-224-5p mainly in an exosome-dependent manner,and then miR-224-5p could be transferred to surrounding tumor cells to regulate cell proliferation in the form of autocrine or ***,ULK2 was characterized as a direct target of miR-224-5p and was downregulated in CRC ***,ULK2 inhibited CRC cell proliferation in a p53-dependent ***,exosome-derived miR-224-5p partially reversed the proliferation regulation of ULK2 on CRC *** Our findings demonstrate that exosome-transmitted miR-224-5p promotes p53-dependent cell proliferation by targeting ULK2 in CRC,which may offer promising targets for CRC prevention and therapy.

关键词： miR-224-5p Exosome ULK2 p53 Cell proliferation Colorectal cancer

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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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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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Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution

arXiv

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arXiv 2024年

作者： Duan, Minghong Qu, Linhao Liu, Shaolei Wang, Manning Digital Medical Research Center Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR image based on the queried coordinate and ensemble nearby features, overlooking the importance of incorporating high-frequency prior information in images, which results in limited performance in reconstructing high-frequency texture details in images. To address this issue, we propose the Local Implicit Wavelet Transformer (LIWT) to enhance the restoration of high-frequency texture details. Specifically, we decompose the features extracted by an encoder into four sub-bands containing different frequency information using Discrete Wavelet Transform (DWT). We then introduce the Wavelet Enhanced Residual Module (WERM) to transform these four sub-bands into high-frequency priors, followed by utilizing the Wavelet Mutual Projected Fusion (WMPF) and the Wavelet-aware Implicit Attention (WIA) to fully exploit the high-frequency prior information for recovering high-frequency details in images. We conducted extensive experiments on benchmark datasets to validate the effectiveness of LIWT. Both qualitative and quantitative results demonstrate that LIWT achieves promising performance in arbitrary-scale SR tasks, outperforming other state-of-the-art methods. The code is available at https://***/dmhdmhdmh/LIWT. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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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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