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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2874 条记录，以下是341-350 订阅

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Explaining Massive-Training Artificial Neural Networks in medical image Analysis Task through Visualizing Functions Within the Models 26th

Explaining Massive-Training Artificial Neural Networks in Me...

引用

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

作者： Jin, Ze Pang, Maolin Yang, Yuqiao Mahdi, Fahad Parvez Qu, Tianyi Sasage, Ren Suzuki, Kenji Tokyo Inst Technol Inst Innovat Res Biomed Artificial Intelligence Res Unit Meguro Kanagawa Japan

ISBN: (纸本)9783031438943;9783031438950

In this study, we proposed a novel explainable artificial intelligence (XAI) technique to explainmassive-training artificial neural networks (MTANNs). Firstly, we optimized the structure of anMTANNto find a compact model that performs equivalently well to the original one. this enables to "condense" functions in a smaller number of hidden units in the network by removing "redundant" units. then, we applied an unsupervised hierarchical clustering algorithm to the function maps in the hidden layers with the single-linkage method. From the clustering and visualization results, we were able to group the hidden units into those with similar functions together and reveal the behaviors and functions of the trained MTANN models. We applied this XAI technique to explain the MTANN model trained to segment liver tumors in CT. the original MTANN model with 80 hidden units (F1 = 0.6894, Dice = 0.7142) was optimized to the one with nine hidden units (F1 = 0.6918, Dice = 0.7005) with almost equivalent performance. the nine hidden units were clustered into three groups, and we found the following three functions: 1) enhancing liver area, 2) suppressing non-tumor area, and 3) suppressing the liver boundary and false enhancement. the results shed light on the "black-box" problem with deep learning (DL) models;and we demonstrated that our proposed XAI technique was able to make MTANN models "transparent".

关键词： Deep Learning Explainable AI (XAI) Visualizing Functions Liver Tumor Segmentation Unsupervised Hierarchical Clustering

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Gene-Induced Multimodal Pre-training for image-Omic Classification 26th

Gene-Induced Multimodal Pre-training for Image-Omic Classifi...

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

作者： Jin, Ting Xie, Xingran Wan, Renjie Li, Qingli Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Histology analysis of the tumor micro-environment integrated with genomic assays is the gold standard for most cancers in modern medicine. this paper proposes a Gene-induced Multimodal Pre-training (GiMP) framework, which jointly incorporates genomics and Whole Slide images (WSIs) for classification tasks. Our work aims at dealing with the main challenges of multi-modality image-omic classification w.r.t. (1) the patient-level feature extraction difficulties from gigapixel WSIs and tens of thousands of genes, and (2) effective fusion considering high-order relevance modeling. Concretely, we first propose a group multi-head self-attention gene encoder to capture global structured features in gene expression cohorts. We design a masked patch modeling paradigm (MPM) to capture the latent pathological characteristics of different tissues. the mask strategy is randomly masking a fixed-length contiguous subsequence of patch embeddings of a WSI. Finally, we combine the classification tokens of paired modalities and propose a triplet learning module to learn high-order relevance and discriminative patient-level information. After pre-training, a simple fine-tuning can be adopted to obtain the classification results. Experimental results on the TCGA dataset show the superiority of our network architectures and our pre-training framework, achieving 99.47% in accuracy for image-omic classification. the code is publicly available at https://***/huangwudiduan/GIMP.

关键词： Multimodal learning Whole slide image classification

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LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion 26th

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy w...

引用

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

作者： Bai, Long Chen, Tong Wu, Yanan Wang, An Islam, Mobarakol Ren, Hongliang Chinese Univ Hong Kong CUHK Dept Elect Engn Hong Kong Peoples R China Univ Sydney Sydney NSW Australia Northeastern Univ Shenyang Peoples R China UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England CUHK Shun Hing Inst Adv Engn Hong Kong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. the multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Moreover, we combine the reverse diffusion procedure to optimize the shallow output further and generate images highly approximate to real ones. the proposed method is compared with eleven state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. the superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible at ***/longbai1006/LLCaps.

关键词： image enhancement

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CTFlow: Mitigating Effects of Computed Tomography Acquisition and Reconstruction with Normalizing Flows 26th

CTFlow: Mitigating Effects of Computed Tomography Acquisitio...

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

作者： Wei, Leihao Yadav, Anil Hsu, William Univ Calif Los Angeles Samueli Sch Engn Dept Elect & Comp Engn Los Angeles CA 90095 USA UCLA David Geffen Sch Med Dept Radiol Sci Med & Imaging Informat Los Angeles CA 90024 USA

ISBN: (纸本)9783031439896;9783031439902

Mitigating the effects of image appearance due to variations in computed tomography (CT) acquisition and reconstruction parameters is a challenging inverse problem. We present CTFlow, a normalizing flows-based method for harmonizing CT scans acquired and reconstructed using different doses and kernels to a target scan. Unlike existing state-of-the-art image harmonization approaches that only generate a single output, flow-based methods learn the explicit conditional density and output the entire spectrum of plausible reconstruction, reflecting the underlying uncertainty of the problem. We demonstrate how normalizing flows reduces variability in image quality and the performance of a machine learning algorithm for lung nodule detection. We evaluate the performance of CTFlow by 1) comparing it with other techniques on a denoising task using the AAPM-Mayo Clinical Low-DoseCTGrandChallenge dataset, and 2) demonstrating consistency in nodule detection performance across 186 real-world low-dose CT chest scans acquired at our institution. CTFlow performs better in the denoising task for both peak signal-to-noise ratio and perceptual quality metrics. Moreover, CTFlow produces more consistent predictions across all dose and kernel conditions than generative adversarial network (GAN)-based image harmonization on a lung nodule detection task. the code is available at https://***/hsu-lab/ctflow.

关键词： image harmonization computed tomography normalizing flows

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MDA-SR: Multi-level Domain Adaptation Super-Resolution for Wireless Capsule Endoscopy images 26th

MDA-SR: Multi-level Domain Adaptation Super-Resolution for W...

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

作者： Liu, Tianbao Chen, Zefeiyun Li, Qingyuan Wang, Yusi Zhou, Ke Xie, Weijie Fang, Yuxin Zheng, Kaiyi Zhao, Zhanpeng Liu, Side Yang, Wei Southern Med Univ Sch Biomed Engn Guangzhou Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc Guangzhou Peoples R China Southern Med Univ Nanfang Hosp Dept Gastroenterol Guangzhou Peoples R China Guangzhou SiDe MedTech Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Super-resolution (SR) of wireless capsule endoscopy (WCE) images is challenging because paired high-resolution (HR) images are not available. An intuitive solution is to simulate paired low-resolution (LR) WCE images from HR electronic endoscopy images for supervised learning. However, the SR model obtained by this method cannot be well adapted to real WCE images due to the large domain gap between electronic endoscopy images and WCE images. To address this issue, we propose a Multi-level Domain Adaptation SR model (MDA-SR) in an unsupervised manner using arbitrary set of WCE images and HR electronic endoscopy images. Our approach implements domain adaptation at the image level and latent level during the degradation and SR processes, respectively. To the best of our knowledge, this is the first work to explore an unsupervised SR approach for WCE images. Furthermore, we design an Endoscopy image Quality Evaluator (EIQE) based on the reference-free image evaluation metric NIQE, which is more suitable for evaluating WCE image quality. Extensive experiments demonstrate that our MDA-SR method outperforms state-of-the-art SR methods both quantitatively and qualitatively.

关键词： Wireless capsule endoscopy Super-resolution Domain adaptation

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Cross-Modality image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. this work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. this can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. this accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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

作者： Zhong, Yuming Wang, Yi Shenzhen Univ Sch Med Med UltraSound Image Comp MUSIC Lab Sch Biomed EngnSmart Med Imaging Learning & Engn Shenzhen Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in the screening and prognosis assessment of high-risk breast cancer. the segmentation of cancerous regions is essential useful for the subsequent analysis of breast MRI. To alleviate the annotation effort to train the segmentation networks, we propose a weakly-supervised strategy using extreme points as annotations for breast cancer segmentation. Without using any bells and whistles, our strategy focuses on fully exploiting the learning capability of the routine training procedure, i.e., the train - fine-tune - retrain process. the network first utilizes the pseudo-masks generated using the extreme points to train itself, by minimizing a contrastive loss, which encourages the network to learn more representative features for cancerous voxels. then the trained network fine-tunes itself by using a similarity-aware propagation learning (SimPLe) strategy, which leverages feature similarity between unlabeled and positive voxels to propagate labels. Finally the network retrains itself by employing the pseudo-masks generated using previous fine-tuned network. the proposed method is evaluated on our collected DCE-MRI dataset containing 206 patients with biopsy-proven breast cancers. Experimental results demonstrate our method effectively fine-tunes the network by using the SimPLe strategy, and achieves a mean Dice value of 81%. Our code is publicly available at https://***/Abner228/SmileCode.

关键词： Breast cancer Weakly-supervised learning medical image segmentation Contrastive learning DCE-MRI

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KA2ER: Knowledge Adaptive Amalgamation of ExpeRts for medical images Segmentation 1st

KA2ER: Knowledge Adaptive Amalgamation of ExpeRts for Medi...

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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： Gao, Shangde Fu, Yichao Liu, Ke Xu, Hongxia Wu, Jian College of Computer Science and Technology Zhejiang University Hangzhou China Liangzhu Laboratory Zhejiang University Hangzhou China WeDoctor Holdings Limited Hangzhou China State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China School of Public Health Zhejiang University Hangzhou China

ISBN: (纸本)9783031870088

Recently, many foundation models for medical image analysis such as MedSAM, SwinUNETR have been released and proven to be useful in multiple tasks. However, considering the inherent heterogeneity and inhomogeneity of real-world medical data, directly applying these models to specific medical image segmentation tasks often leads to negative domain shift effects, which can severely weaken the model’s segmentation capabilities. To this end, we propose an adaptive amalgamation knowledge framework that aims to train a versatile foundation model to handle the joint goals of multiple expert models, each specialized for a distinct task. Specifically, we first train an nnUNet-based expert model for each task, and reuse the pre-trained SwinUNTER as the target foundation model. then, the input data for all challenging tasks are encoded in the foundation model and the expert models, respectively, and their backbone features are jointly projected into the adaptive amalgamation layer. Within the hidden layer, the hierarchical attention mechanisms are designed to achieve adaptive merging of the target model to the hidden layer feature knowledge of all experts, which significantly reduces the domain shift arising from the inter-task differences. Finally, the gold amalgamated features and the prompt features are fed into the mask decoder to obtain the segmentation results. Extensive experiments conducted in these challenging tasks demonstrate the effectiveness and adaptability of our foundation model for real-world medical image segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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DRMC: A Generalist Model with Dynamic Routing for Multi-center PET image Synthesis 1

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

作者： Yang, Zhiwen Zhou, Yang Zhang, Hui Wei, Bingzheng Fan, Yubo Xu, Yan Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Key Lab Biomech & MechMinist EducState Key Lab Beijing 100191 Peoples R China Tsinghua Univ Dept Biomed Engn Beijing 100084 Peoples R China Xiaomi Corp Beijing 100085 Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. the generalizability of existing methods can still be suboptimal for a multi-center study due to domain shifts, which result from non-identical data distribution among centers with different imaging systems/protocols. While some approaches address domain shifts by training specialized models for each center, they are parameter inefficient and do not well exploit the shared knowledge across centers. To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge. However, the generalist model can suffer from the center interference issue, i.e. the gradient directions of different centers can be inconsistent or even opposite owing to the non-identical data distribution. To mitigate such interference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available at: https://***/Yaziwel/Multi-Center-PET-image-Synthesis.

关键词： Multi-Center Positron Emission Tomography Synthesis Generalist Model Dynamic Routing

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A Two-Stage Self-Supervised Learning Framework for Automated Whole Heart Segmentation in CT and MRI: Addressing Challenges in Cardiac Imaging 1st

A Two-Stage Self-Supervised Learning Framework for Automated...

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作者： Qayyum, Abdul Mazher, Moona Niederer, Steven A. Faculty of Medicine National Heart and Lung Institute Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031870088

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, highlighting the need for precise diagnostic and therapeutic strategies. Whole heart segmentation (WHS) from medical images is vital for understanding cardiac anatomy, assessing disease progression, and guiding treatments. However, WHS poses significant challenges due to the dynamic shape changes of the heart during the cardiac cycle, motion artifacts, low contrast-to-noise ratio, and variability from multi-center data. Additionally, CT and MRI modalities each present unique difficulties: CT provides high spatial resolution but poor soft-tissue contrast, while MRI offers excellent soft-tissue contrast but is prone to artifacts. To address these challenges, we propose a two-stage method. In the first stage, the DINOv2 framework leverages a 3D Vision-LSTM (xLSTM) model for self-supervised learning on the limited, unlabeled WHS++ dataset to overcome the scarcity of annotated data. In the second stage, we fine-tune the model for segmentation by freezing the encoder and optimizing the decoder. Our approach is rigorously evaluated on the WHS++ dataset for both CT and MRI modalities, demonstrating superior performance compared to state-of-the-art deep learning models in accurately segmenting cardiac structures. Our proposed solution achieved an average Dice Coefficient of 0.9414 using CT and 0.8891 using MRI dataset. this work advances the field of cardiac imaging by providing a robust, generalizable solution for automated whole heart segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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