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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical images, CARE 2024 Held in Conjunction with 27th International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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Revisiting the Benefits of Knowledge Distillation against Adversarial Examples 6

Revisiting the Benefits of Knowledge Distillation against Ad...

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6th International Conference on Data Science and Information Technology, DSIT 2023

作者： Li, Jiawen Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350304442

Deep neural networks (DNNs) have always been a popular base model in many image classification tasks. However, some recent works suggest that there are some man made images will easily lead DNNs give wrong predictions. These images are generated by adding some invisible perturbations into clean images, which are the so-called adversarial examples. Since they are almost similar to original data but can fool DNNs, adversarial examples can bring the insecurity to many life-critical applications. Adversarial knowledge distillation is a positive defense method to attach improved robustness to the target DNNs against adversarial examples. In distillation, student models are trained by soft labels coming from robust pre-trained teacher networks. However, it is hard for researchers to ensure that supervised information comes from a better teacher model. In this paper, We focus on four potential factors of the teacher that may have an effect to the distillation performance of student model, and investigate whether and when they can boost the model robustness against adversarial attacks. Multiple quantitative experiment illustrate that these factors have impact on the distillation process and these results can be considered to choose a better teacher model. Further studies can focus on the combination of adversarial knowledge distillation and other defense schemes. Empirical results in this paper may also help select proper teacher in new complex distillation tasks. © 2023 IEEE.

关键词： Distillation

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Applications of artificial intelligence to lipid nanoparticle delivery

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Particuology 2024年第7期90卷 88-97页

作者： Ye Yuan Yuqi Wu Jiabei Cheng Kaiyuan Yang Yilin Xia Hongguang Wu Xiaoyong Pan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaChina Songjiang Research Institute Songjiang District Central HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.

关键词： Artificial intelligence Lipid nanoparticles Antibody Targeted conjugation

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A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET images 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2022

作者： Sun, Xiao An, Chengyang Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and neck (H &N) cancer is one of the most prevalent cancers [1]. In its treatment and prognosis analysis, tumors and metastatic lymph nodes may play an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. The framework consists of three steps. The first step is to locate the head region in CT images. The second step is a coarse segmentation, to locate the tumor and lymph region of interest (ROI) from the head region. The last step is a fine segmentation, to get the final precise predictions of tumors and metastatic lymph nodes, where we proposed a ensembling refinement model. This framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[2, 3] as team SJTU426. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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ClusVPR: Efficient Visual Place recognition With Clustering-Based Weighted Transformer

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1038-1049页

作者： Xu, Yifan Shamsolmoali, Pourya Zareapoor, Masoume Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China East China Normal University School of Communication Shanghai200240 China University of York YorkYO10 5DD United Kingdom

Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is a difficult task due to duplicate regions and insufficient attention to small objects in complex scenes, resulting in recognition deviations. In this article, we present ClusVPR, a novel approach that tackles the specific issues of redundant information in duplicate regions and representations of small objects. Different from existing methods that rely on convolutional neural networks (CNNs) for feature map generation, ClusVPR introduces a unique paradigm called clustering-based weighted transformer network (CWTNet). CWTNet uses the power of clustering-based weighted feature maps and integrates global dependencies to effectively address visual deviations encountered in large-scale VPR problems. We also introduce the optimized-VLAD (OptLAD) layer, which significantly reduces the number of parameters and enhances model efficiency. This layer is specifically designed to aggregate the information obtained from scale-wise image patches. Additionally, our pyramid self-supervised strategy focuses on extracting representative and diverse features from scale-wise image patches rather than from entire images. This approach is essential for capturing a broader range of information required for robust VPR. Extensive experiments on four VPR datasets show our model's superior performance compared to existing models while being less complex. © 2024 IEEE.

关键词： Self-supervised learning

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ChromTR:chromosome detection in raw metaphase cell images via deformable transformers

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Frontiers of Medicine 2024年第6期18卷 1100-1114页

作者： Chao Xia Jiyue Wang Xin You Yaling Fan Bing Chen Saijuan Chen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai200240China Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational Medicine at ShanghaiRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200025China

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging *** this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell *** incorporates semantic feature learning and class distribution learning into a unified DETR-based detection ***,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise ***,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic *** provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution *** evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset *** proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal *** extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image *** these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.

关键词： chromosome detection deformable transformer metaphase cell image distribution reasoning

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Learnable Relation with Triplet Formulation for Semi-supervised Medical image Classification 7th

Learnable Relation with Triplet Formulation for Semi-super...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Sun, Yiming Xie, Zhiqiang Fang, Kun Tu, Enmei Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811992964

For medical image classification, annotations for images are laborious and expensive, which is suitable for the application of semi-supervised learning. Mainstream semi-supervised learning methods develop a consistency regularization to leverage the unlabeled data but they neglect the relations among data. This paper proposes a novel learnable relation semi-supervised method with triplet formulation to not only jointly achieve feature extraction and distance metric learning but restrict the relations among features properly. With the learnable distance metric, the proposed method could learn the features and the metric via one single network to much better characterize the relations among features. Besides, triplet formulation is employed to constraint the relations among features. Experiments on skin lesion diagnosis data set indicate that the proposed method outperforms other state-of-the-art semi-supervised learning methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image classification

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Follicular Unit Registration Based on Binocular Stereo Vision for Hair Transplantation Surgery Robot 6

Follicular Unit Registration Based on Binocular Stereo Visio...

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6th International Conference on Data Science and Information Technology, DSIT 2023

作者： Wang, Weiduo Liu, Jinfeng Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation China Shanghai Jiao Tong University Institute of Medical Robotics China

ISBN: (纸本)9798350304442

In this paper, we focus on the follicular unit registration problem for hair transplantation surgery robots based on binocular stereo vision system. The follicular units in both images are detected with YOLO V5 network. Because of the dull and similar pattern of the follicular units, previous regular methods which are pixel-based are reported with misalignment and poor accuracy. To solve this problem, we propose the context shape to describe the feature of the follicular unit. The distribution of units surrounding the target unit is estimated as the context shape. Then, Otsu's method is used to segment hairs and Hungarian algorithm is used to match the hairs in the matched follicular unit. Then, the hairs are projected to 3D coordinate system and reconstructed. The experiments on the generated hair dataset demonstrate that the proposed method achieves both high accuracy and speed. © 2023 IEEE.

关键词： Stereo vision

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A Branch-sensitive 3D Convolutional Neural Networks for Airway Segmentation 5

A Branch-sensitive 3D Convolutional Neural Networks for Airw...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Zhao, Yingjie Liu, Yusheng Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Department of Automation China

ISBN: (纸本)9781665479677

CT scans are an exceptional tool for quantitative airway analysis. Due to the complex voxel connectivity and branch structure of the airway, precise and fine segmentation results are difficult to accomplish. We propose a fully automated and end-to-end approach for airway segmentation and branch detection in chest CT based on U-Net architecture. We introduce the SE Normalization as a module for feature calibration and the hard region adaptation loss function based on cross entropy (HRA-CE) for dynamically maintaining class balance throughout the training period. We present a brand-new metric Branch DSC designed exclusively to assess the branch structure. We validate the suggested method on a dataset of 46 airway samples, and the experimental findings demonstrate that our proposed method significantly improves branch detection and segmentation continuity. © 2022 IEEE.

关键词： Computerized tomography

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Dynamic Frame Interpolation in Wavelet Domain

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IEEE Transactions on image Processing 2023年 32卷 5296-5309页

作者： Kong, Lingtong Jiang, Boyuan Luo, Donghao Chu, Wenqing Tai, Ying Wang, Chengjie Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Department of Automation Shanghai200240 China Tencent Youtu Laboratory Shanghai200233 China Nanjing University School of Intelligence Science and Technology Suzhou215163 China

Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. However, the spatial redundancy when synthesizing the target frame has not been fully explored, that can result in lots of inefficient computation. On the other hand, the computation compression degree in frame interpolation is highly dependent on both texture distribution and scene motion, which demands to understand the spatial-temporal information of each input frame pair for a better compression degree selection. In this work, we propose a novel two-stage frame interpolation framework termed WaveletVFI to address above problems. It first estimates intermediate optical flow with a lightweight motion perception network, and then a wavelet synthesis network uses flow aligned context features to predict multi-scale wavelet coefficients with sparse convolution for efficient target frame reconstruction, where the sparse valid masks that control computation in each scale are determined by a crucial threshold ratio. Instead of setting a fixed value like previous methods, we find that embedding a classifier in the motion perception network to learn a dynamic threshold for each sample can achieve more computation reduction with almost no loss of accuracy. On the common high resolution and animation frame interpolation benchmarks, proposed WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts. © 1992-2012 IEEE.

关键词： Wavelet transforms

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