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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Weakly Supervised Volumetric image Segmentation with Deformed Templates 25th

Weakly Supervised Volumetric Image Segmentation with Deforme...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wickramasinghe, Udaranga Jensen, Patrick Shah, Mian Yang, Jiancheng Fua, Pascal Ecole Polytech Fed Lausanne Lausanne Switzerland DTU Lyngby Denmark Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9783031164439;9783031164422

there are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume segmentation that is truly weakly-supervised in the sense that we only need to provide a sparse set of 3D points on the surface of target objects instead of detailed 2D masks. We use the 3D points to deform a 3D template so that it roughly matches the target object outlines and we introduce an architecture that exploits the supervision it provides to train a network to find accurate boundaries. We evaluate our approach on Computed Tomography (CT), Magnetic Resonance imagery (MRI) and Electron Microscopy (EM) image datasets and show that it substantially reduces the required amount of effort.

关键词： Magnetic resonance

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TransDUNet:image segmentation of artificial joint based on TransUNet 8

TransDUNet:Image segmentation of artificial joint based on T...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Tang, Jingkun Sichuan University College of Computer Science Chengdu China

ISBN: (纸本)9798350302455

Traditional convolution has limited ability to capture long-range dependencies of images, which is crucial for semantic segmentation in medical images, where spatial relationships between different anatomical structures are compound and non-local. the long-range dependencies in medical images can be captured by multi-head self-attention mechanisms, which is essential for complex and non-local spatial relationships between different anatomical structures. the receptive field of standard up-sampling convolutions is limited, which can affect the accuracy of segmentation predictions. Dilated convolutions can expand the receptive field and capture multi-scale contextual information. this paper proposes a new deep learning architecture called TransDUNet for semantic segmentation of artificial joint implants in medical images. Based on TransUNet, We replace the traditional convolution in the encoder up-sampling module of TransUNet with the module consisting of multiple dilated convolutions with different dilation rates to Incorporating multi-scale information, Dense skip connections are utilized to enhance feature reuse and gradient flow, thereby improving the performance and generalization ability of the model. the proposed algorithm's segmentation performance outperforms recently developed networks, according to experiments, which demonstrate that its mDSC and mIoU are 94.1% and 88.68%, respectively. © 2023 IEEE.

关键词： Convolution

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Privacy Preserving image Registration 25th

Privacy Preserving Image Registration

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Taiello, Riccardo Onen, Melek Humbert, Olivier Lorenzi, Marco Inria Epione Res Project Sophia Antipolis France EURECOM Sophia Antipolis France Univ Cote Azur Nice France

ISBN: (纸本)9783031164460;9783031164453

image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current literature on image registration is generally based on the assumption that images are usually accessible to the researcher, from which the spatial transformation is subsequently estimated. this common assumption may not be met in current practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to share the image content in clear form. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we first propose to optimize the underlying image registration operations using gradient approximations. We further revisit the use of homomorphic encryption and use a packing method to allow the encryption and multiplication of large matrices more efficiently. We demonstrate our privacy preserving framework in linear and non-linear registration problems, evaluating its accuracy and scalability with respect to standard image registration. Our results show that privacy preserving image registration is feasible and can be adopted in sensitive medical imaging applications.

关键词： image registration Privacy enhancing technologies Trustworthiness

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BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis 26th

BrainUSL: Unsupervised Graph Structure Learning for Function...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhang, Pengshuai Wen, Guangqi Cao, Peng Yang, Jinzhu Zhang, Jinyu Zhang, Xizhe Zhu, Xinrong Zaiane, Osmar R. Wang, Fei Northeastern Univ Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang Peoples R China Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang Peoples R China Nanjing Med Univ Biomed Engn & Informat Nanjing Peoples R China Nanjing Med Univ Affiliated Nanjing Brain Hosp Early Intervent Unit Dept Psychiat Nanjing Peoples R China Univ Alberta Amii Edmonton AB Canada

ISBN: (纸本)9783031439926;9783031439933

the functional connectivity (FC) between brain regions is usually estimated through a statistical dependency method with functional magnetic resonance imaging (fMRI) data. It inevitably yields redundant and noise connections, limiting the performance of deep supervised models in brain disease diagnosis. Besides, the supervised signals of fMRI data are insufficient due to the shortage of labeled data. To address these issues, we propose an end-to-end unsupervised graph structure learning method for sufficiently capturing the structure or characteristics of the functional brain network itself without relying on manual labels. More specifically, the proposed method incorporates a graph generation module for automatically learning the discriminative graph structures of functional brain networks and a topology-aware encoding module for sufficiently capturing the structure information. Furthermore, we also design view consistency and correlation-guided contrastive regularizations. We evaluated our model on two real medical clinical applications: the diagnosis of Bipolar Disorder (BD) and Major Depressive Disorder (MDD). the results suggest that the proposed method outperforms state-of-the-art methods. In addition, our model is capable of identifying associated biomarkers and providing evidence of disease association. To the best of our knowledge, our work attempts to construct learnable functional brain networks with unsupervised graph structure learning. Our code is available at https://***/IntelliDAL/Graph/tree/main/BrainUSL.

关键词： Functional connectivity analysis Graph structure learning Unsupervised learning fMRI

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Momentum Contrastive Voxel-Wise Representation Learning for Semi-supervised Volumetric medical image Segmentation 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： You, Chenyu Zhao, Ruihan Staib, Lawrence H. Duncan, James S. Yale Univ Elect Engn New Haven CT 06520 USA Univ Texas Austin Elect & Comp Engn Austin TX USA Yale Sch Med Radiol & Biomed Imaging New Haven CT USA Yale Univ Biomed Engn New Haven CT USA

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Contrastive learning (CL) aims to learn useful representation without relying on expert annotations in the context of medical image segmentation. Existing approaches mainly contrast a single positive vector (i.e., an augmentation of the same image) against a set of negatives within the entire remainder of the batch by simply mapping all input features into the same constant vector. Despite the impressive empirical performance, those methods have the following shortcomings: (1) it remains a formidable challenge to prevent the collapsing problems to trivial solutions;and (2) we argue that not all voxels within the same image are equally positive since there exist the dissimilar anatomical structures with the same image. In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions. Specifically, we first introduce a novel CL strategy to ensure feature diversity promotion among the 3D representation dimensions. We train the framework through bi-level contrastive optimization (i.e., low-level and high-level) on 3D images. Experiments on two benchmark datasets and different labeled settings demonstrate the superiority of our proposed framework. More importantly, we also prove that our method inherits the benefit of hardness-aware property from the standard CL approaches.

关键词： Contrastive learning Semi-supervised learning medical image segmentation

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Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning 26th

Prompt-MIL: Boosting Multi-instance Learning Schemes via Tas...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhang, Jingwei Kapse, Saarthak Ma, Ke Prasanna, Prateek Saltz, Joel Vakalopoulou, Maria Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Snap Inc New York NY USA Univ Paris Saclay Cent Supelec Paris France

ISBN: (纸本)9783031439926;9783031439933

Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-learning methods. Current state of the art methods are based on multi-instance learning schemes (MIL), which usually rely on pretrained features to represent the instances. Due to the lack of task-specific annotated data, these features are either obtained from well-established backbones on natural images, or, more recently from self-supervised models pretrained on histopathology. However, both approaches yield task-agnostic features, resulting in performance loss compared to the appropriate task-related supervision, if available. In this paper, we show that when task-specific annotations are limited, we can inject such supervision into downstream task training, to reduce the gap between fully task-tuned and task agnostic features. We propose Prompt-MIL, an MIL framework that integrates prompts into WSI classification. Prompt-MIL adopts a prompt tuning mechanism, where only a small fraction of parameters calibrates the pretrained features to encode task-specific information, rather than the conventional full fine-tuning approaches. Extensive experiments on three WSI datasets, TCGA-BRCA, TCGA-CRC, and BRIGHT, demonstrate the superiority of Prompt-MIL over conventional MIL methods, achieving a relative improvement of 1.49%-4.03% in accuracy and 0.25%-8.97% in AUROC while using fewer than 0.3% additional parameters. Compared to conventional full fine-tuning approaches, we fine-tune less than 1.3% of the parameters, yet achieve a relative improvement of 1.29%-13.61% in accuracy and 3.22%-27.18% in AUROC and reduce GPU memory consumption by 38%-45% while training 21%-27% faster.

关键词： Whole slide image classification Multiple instance learning Prompt tuning

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Prediction of Spherical Equivalent with Vanilla ResNet 1

引用

Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

作者： Li, Huayu Zhu, Wenhui Chen, Xiwen Wang, Yalin Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States School of Computing Clemson University ClemsonSC United States

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Recently, an increasing of deep learning models has been introduced to address various fundus image tasks, e.g. segmentation, classification, and enhancement. Concurrently, this emergence has been accompanied by the development of increasingly intricate model architectures and a parameter surge. However, these methods have overlooked the crucial significance of the intrinsic characteristics of the data. In this regard, we posit that a vanilla ResNet, when coupled with appropriate data augmentation, can also achieve a favorable performance. this study assesses the performance of the foundational deep learning architecture, the standard Residual Network (ResNet), in predicting Spherical Equivalent (SE) values. Spherical Equivalent (SE) is a crucial parameter in optometry employed for prescribing eyeglasses to correct vision. Our study intends to demonstrate the effectiveness of ResNet with proper data augmentations in achieving precise SE predictions without additional enhancements. Utilizing a vanilla ResNet50 model, we secured the third position in the Myopic Maculopathy Analysis Challenge 2023, task 3. Remarkably, our findings reveal that the unaltered deployment of ResNet yields exceptional predictive performance for estimating SE, highlighting the architectural prowess of simple models in a vital yet frequently demanding optometric context. Our software package is available at https://***/HuayuLiArizona/Prediction-of-Spherical-Equivalent-With-Vanilla-ResNet. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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TransEM: Residual Swin-Transformer Based Regularized PET image Reconstruction 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Hu, Rui Liu, Huafeng Zhejiang Univ Dept Opt Engn State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China Jiaxing Key Lab Photon Sensing Intelligent Imagin Jiaxing 314000 Peoples R China Zhejiang Univ Jiaxing Res Inst Intelligent Opt & Photon Res Ctr Jiaxing 314000 Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Positron emission tomography (PET) image reconstruction is an ill-posed inverse problem and suffers from high level of noise due to limited counts received. Recently deep neural networks especially convolutional neural networks (CNN) have been successfully applied to PET image reconstruction. However, the local characteristics of the convolution operator potentially limit the image quality obtained by current CNN-based PET image reconstruction methods. In this paper, we propose a residual swin-transformer based regularizer (RSTR) to incorporate regularization into the iterative reconstruction framework. Specifically, a convolution layer is firstly adopted to extract shallow features, then the deep feature extraction is accomplished by the swin-transformer layer. At last, both deep and shallow features are fused with a residual operation and another convolution layer. Validations on the realistic 3D brain simulated low-count data show that our proposed method outperforms the state-of-the-art methods in both qualitative and quantitative measures.

关键词： Positron Emission Tomography (PET) image reconstruction Model-based deep learning Transformer

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NVUM: Non-volatile Unbiased Memory for Robust medical image Classification 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Liu, Fengbei Chen, Yuanhong Tian, Yu Liu, Yuyuan Wang, Chong Belagiannis, Vasileios Carneiro, Gustavo Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Otto Guericke Univ Magdeburg Magdeburg Germany

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Real-world large-scale medical image analysis (MIA) datasets have three challenges: 1) they contain noisy-labelled samples that affect training convergence and generalisation, 2) they usually have an imbalanced distribution of samples per class, and 3) they normally comprise a multi-label problem, where samples can have multiple diagnoses. Current approaches are commonly trained to solve a subset of those problems, but we are unaware of methods that address the three problems simultaneously. In this paper, we propose a new training module called Non-Volatile Unbiased Memory (NVUM), which non-volatility stores running average of model logits for a new regularization loss on noisy multi-label problem. We further unbias the classification prediction in NVUM update for imbalanced learning problem. We run extensive experiments to evaluate NVUM on new benchmarks proposed by this paper, where training is performed on noisy multi-label imbalanced chest X-ray (CXR) training sets, formed by Chest-Xray14 and CheXpert, and the testing is performed on the clean multi-label CXR datasets OpenI and PadChest. Our method outperforms previous state-of-the-art CXR classifiers and previous methods that can deal with noisy labels on all evaluations.

关键词： Chest X-ray classification Multi-label classification Imbalanced classification

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Edge computing-Based Cloud Platform for Snakebite assisted Diagnosis 23

Edge Computing-Based Cloud Platform for Snakebite Assisted D...

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2023 8th international conference on Biomedical Signal and image Processing, ICBIP 2023

作者： Liu, Yehui Xu, Aobo Zeng, Hui Huang, Juneng Liang, Mingxian Luo, Wei Zeng, Zhao Zhang, Xuejun School of Computer Electronics and Information Guangxi University Guangxi Nanning China Wuzhou Hospital of Traditional Chinese Medicine Guangxi Nanning China

ISBN: (纸本)9798400707698

Snakebite is a major global public health problem, with more than 5 million snakebite victims recorded each year. Timely diagnosis and appropriate treatment of snakebite are the keys to preventing serious complications and improving the patient's prognosis. However, most medical institutions lack experience in snakebite treatment, which is prone to misdiagnosis or even missed diagnosis, and serious cases endanger the lives of patients. Artificial intelligence is widely used in computer-aided diagnosis systems (CAD) and plays a vital role in snakebite recognition. In recent years, research has tended to build models, ignoring data preparation and analysis. therefore, there is an urgent need for a rapid and accurate diagnostic method for snakebite to assist clinical diagnostic decision-making. We propose a snakebite auxiliary diagnosis system combining edge computing and machine learning technology. the system has the advantages of simple deployment, strong flexibility, and simple operation and is not limited by the equipment conditions of some grassroots hospitals. Experimental results show that this system can quickly and accurately diagnose the types of venomous snakes, overcome the limitations of traditional snakebite diagnosis methods in various aspects, and provide reliable real-time service tools for medical professionals, especially those in remote areas, allowing rapid diagnosis and treatment of snakebite cases, ultimately reducing morbidity and mortality. © 2023 ACM.

关键词： Information services

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