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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

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15th international Workshop on Machine Learning in medical Imaging, MLMI 2024 was held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

15th International Workshop on Machine Learning in Medical I...

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15th international Workshop on Machine Learning in medical Imaging, MLMI 2024 was held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031732928

The proceedings contain 63 papers. The special focus in this conference is on Machine Learning in medical Imaging. The topics include: IRUM: An image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-View Ultrasound images;Classification, Regression and Segmentation Directly from K-Space in Cardiac MRI;DDSB: An Unsupervised and Training-Free Method for Phase Detection in Echocardiography;mitral Regurgitation Recogniton Based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification;Deep Reinforcement Learning with Multiple Centerline-Guidance for Localization of Left Atrial Appendage Orifice from CT images;Lung-CADex: Fully Automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT images;CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression;vision Transformer Model for Automated End-to-End Radiographic Assessment of Joint Damage in Psoriatic Arthritis;corticalEvolve: Age-Conditioned Ordinary Differential Equation Model for Cortical Surface Reconstruction;CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning;atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound images;Leveraging IHC Staining to Prompt HER2 Status Prediction from HE-Stained Histopathology Whole Slide images;VIMs: Virtual Immunohistochemistry Multiplex Staining via Text-to-Stain Diffusion Trained on Uniplex Stains;structural-Connectivity-Guided Functional Connectivity Representation for Multi-modal Brain Disease Classification;Clinical Brain MRI Super-Resolution with 2D Slice-Wise Diffusion Model;Low-to-High Frequency Progressive K-Space Learning for MRI Reconstruction;LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging;7T-Like T1-Weighted and TOF MRI Synthesis from 3T MRI with Multi-contrast Complementary Deep Learning;A Probabilistic Hadamard

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Understanding Silent Failures in medical image Classification 1

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

作者： Bungert, Till J. Kobelke, Levin Jaeger, Paul F. German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany DKFZ Helmholtz Imaging Heidelberg Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that are robust enough to avoid failures in the first place, or by detecting remaining failures using confidence scoring functions (CSFs). A predominant source of failures in image classification is distribution shifts between training data and deployment data. To understand the current state of silent failure prevention in medical imaging, we conduct the first comprehensive analysis comparing various CSFs in four biomedical tasks and a diverse range of distribution shifts. Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required. To facilitate this, we introduce SF-Visuals, an interactive analysis tool that uses latent space clustering to visualize shifts and failures. On the basis of various examples, we demonstrate how this tool can help researchers gain insight into the requirements for safe application of classification systems in the medical domain. The open-source benchmark and tool are at: https://***/IML-DKFZ/sf-visuals.

关键词： Failure detection Distribution shifts Benchmark

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Few Shot medical image Segmentation with Cross Attention Transformer 26th

Few Shot Medical Image Segmentation with Cross Attention Tra...

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

作者： Lin, Yi Chen, Yufan Cheng, Kwang-Ting Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with manual annotations. However, manual annotation is expensive in the field of medical image analysis, which requires domain-specific expertise. To address this challenge, few-shot learning has the potential to learn new classes from only a few examples. In this work, we propose a novel framework for few-shot medical image segme ntation, termed CAT-Net, based on cross masked attention Transformer. Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features. We further design an iterative refinement framework that refines the query image segmentation iteratively and promotes the support feature in turn. We validated the proposed method on three public datasets: Abd-CT, Abd-MRI, and Card-MRI. Experimental results demonstrate the superior performance of our method compared to state-of-the-art methods and the effectiveness of each component. Source code: https://github. com/hust- linyi/CAT- Net.

关键词： Few Shot Cross Attention Iterative Refinement

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Enhanced Fetal Development Assessment via Contour Detection and CRL Estimation 3

Enhanced Fetal Development Assessment via Contour Detection ...

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3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Sriraam, Natarajan Chinta, Babu Suresh, Seshadhri Sudharshan, Suresh Ramaiah Institute of Technology Center for Medical Electronics and Computing Department of Medical Electronics Engineering Bangalore India Center for Medical Electronics and Computing Ramaiah Institute of Technology Bangalore India Mediscans Pvt Ltd Chennai India

ISBN: (纸本)9798350313703

Accurate fetal growth evaluation is critical for monitoring pregnancy health and delivering appropriate prenatal treatment. We provide a unique strategy for improving the precision of fetal growth assessment by merging sophisticated contour recognition algorithms with Crown-Rump Length (CRL) estimate in this work. We establish a more complete and reliable approach of measuring fetal growth and development by integrating cutting-edge computer vision and deep learning methods. Our technique uses powerful image processing methods to determine the shape of the fetal anatomy in ultrasound images, overcoming hurdles such as noise, artifacts, and fetal positional fluctuations. Following contour identification, exact CRL length estimate is performed, which is a critical predictor of gestational age and fetal well-being. The combination of these two components considerably improves accuracy of fetal growth assessment. © 2024 IEEE.

关键词： image enhancement

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All You Need Is a Guiding Hand: Mitigating Shortcut Bias in Deep Learning Models for medical Imaging 2nd

All You Need Is a Guiding Hand: Mitigating Shortcut Bias in...

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2nd international Workshop on Fairness of AI in medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on medical image computing and computer assisted interventions, MICCAI 2024

作者： Boland, Christopher Anderson, Owen Goatman, Keith A. Hipwell, John Tsaftaris, Sotirios A. Dahdouh, Sonia Canon Medical Research Europe EdinburghEH6 5NP United Kingdom School of Engineering The University of Edinburgh EdinburghEH9 3FG United Kingdom

ISBN: (纸本)9783031727863

Deep learning models for medical imaging are prone to learning shortcut solutions that rely on spurious correlations instead of clinically meaningful features, leading to poor generalization to new data. We propose an oracle-guided training scheme that encourages a student model to learn robust features in the presence of shortcuts. Our method regulates prediction confidence across intermediate network layers, significantly reducing shortcut impact. We evaluate our approach on CIFAR10, CheXpert, and ISIC 2017 datasets using ResNet18 and DenseNet121 architectures. Consistently, we outperform a model trained using Empirical Risk Minimization on a dataset containing a shortcut. In several cases, we close the gap on our clean baseline to the point that there is no statistically significant difference in performance. We also address the practical challenge of obtaining a clean oracle model, enhancing the method’s real-world applicability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Maximum Entropy on Erroneous Predictions: Improving Model Calibration for medical image Segmentation 1

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

作者： Larrazabal, Agostina J. Martinez, Cesar Dolz, Jose Ferrante, Enzo UNL CONICET Res Inst Signals Syst & Computat Intelligence SinciFICH Santa Fe Argentina ETS Montreal LIVIA Montreal PQ Canada Tryolabs Montevideo Uruguay

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Modern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimates, even in situations of high uncertainty, leading to poorly calibrated and unreliable models. In this work we introduce Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for segmentation networks which selectively penalizes overconfident predictions, focusing only on misclassified pixels. Our method is agnostic to the neural architecture, does not increase model complexity and can be coupled with multiple segmentation loss functions. We benchmark the proposed strategy in two challenging segmentation tasks: white matter hyperintensity lesions in magnetic resonance images (MRI) of the brain, and atrial segmentation in cardiac MRI. The experimental results demonstrate that coupling MEEP with standard segmentation losses leads to improvements not only in terms of model calibration, but also in segmentation quality.

关键词： image segmentation uncertainty calibration

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Unsupervised Learning for Feature Extraction and Temporal Alignment of 3D+t Point Clouds of Zebrafish Embryos 26th

Unsupervised Learning for Feature Extraction and Temporal Al...

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

作者： Chen, Zhu Laube, Ina Stegmaier, Johannes Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany

ISBN: (纸本)9783031439926;9783031439933

Zebrafish are widely used in biomedical research and developmental stages of their embryos often need to be synchronized for further analysis. We present an unsupervised approach to extract descriptive features from 3D+t point clouds of zebrafish embryos and subsequently use those features to temporally align corresponding developmental stages. An autoencoder architecture is proposed to learn a descriptive representation of the point clouds and we designed a deep regression network for their temporal alignment. We achieve a high alignment accuracy with an average mismatch of only 3.83 min over an experimental duration of 5.3 h. As a fully-unsupervised approach, there is no manual labeling effort required and unlike manual analyses the method easily scales. Besides, the alignment without human annotation of the data also avoids any influence caused by subjective bias.

关键词： Embryo Development Point Clouds Unsupervised Learning Autoencoder

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

15th International Workshop on Computational Diffusion MRI, ...

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

ISBN: (纸本)9783031869198

The proceedings contain 19 papers. The special focus in this conference is on Computational Diffusion MRI. The topics include: Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains;synthesizing 3D Axon Morphology: Springs are All We Need;Randomly COMMITting: Iterative Convex Optimization for Microstructure-Informed Tractography;AID-DTI: Accelerating High-Fidelity Diffusion Tensor Imaging with Detail-Preserving Model-Based Deep Learning;multi-dimensional Parameter Space Exploration for Streamline-Specific Tractography;cross-Domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction;can Transfer Learning Improve Supervised Segmentation of White Matter Bundles in Glioma Patients?;image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI;QID2: An image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data;Ts-FWE: Token-Aware Single-Shell Free Water Estimation for Brain Diffusion MRI;Assessing Early Motor System Degeneration in the Spinal Cord of ALS Patients Using Diffusion MRI: An Exploratory Study;RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation;introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization;Learning Low-Rank Tensor Approximation for GPU-Based Tractography;deep Multivariate Autoencoder for Capturing Complexity in Brain Structure and Behaviour Relationships;heritability and Genetic Correlations Along the Corticospinal Tract;corpus Callosum Parcellation Methods: What Can Tractography Tell Us About Them?.

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MedGen3D: A Deep Generative Framework for Paired 3D image and Mask Generation 26th

MedGen3D: A Deep Generative Framework for Paired 3D Image an...

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

作者： Han, Kun Xiong, Yifeng You, Chenyu Khosravi, Pooya Sun, Shanlin Yan, Xiangyi Duncan, James S. Xie, Xiaohui Univ Calif Irvine Irvine CA 92697 USA Yale Univ New Haven CT USA

ISBN: (纸本)9783031439063;9783031439070

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations. However, no prior studies have explored generating complete 3D volumetric images with masks. In this paper, we present MedGen3D, a deep generative framework that can generate paired 3D medical images and masks. First, we represent the 3D medical data as 2D sequences and propose the Multi-Condition Diffusion Probabilistic Model (MC-DPM) to generate multi-label mask sequences adhering to anatomical geometry. Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks. Our proposed framework guarantees accurate alignment between synthetic images and segmentation maps. Experiments on 3D thoracic CT and brain MRI datasets show that our synthetic data is both diverse and faithful to the original data, and demonstrate the benefits for downstream segmentation tasks. We anticipate that MedGen3D's ability to synthesize paired 3D medical images and masks will prove valuable in training deep learning models for medical imaging tasks.

关键词： Deep Generative Framework 3D Volumetric images with Masks Fidelity and Diversity Segmentation

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SwIPE: Efficient and Robust medical image Segmentation with Implicit Patch Embeddings 26th

SwIPE: Efficient and Robust Medical Image Segmentation with ...

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

作者： Zhang, Yejia Gu, Pengfei Sapkota, Nishchal Chen, Danny Z. Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9783031439032;9783031439049

Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions. Although effective, this paradigm is spatially inflexible, scales poorly to higher-resolution images, and lacks direct understanding of object shapes. To address these limitations, some recent works utilized implicit neural representations (INRs) to learn continuous representations for segmentation. However, these methods often directly adopted components designed for 3D shape reconstruction. More importantly, these formulations were also constrained to either point-based or global contexts, lacking contextual understanding or local fine-grained details, respectively-both critical for accurate segmentation. To remedy this, we propose a novel approach, SwIPE (Segmentation with Implicit Patch Embeddings), that leverages the advantages of INRs and predicts shapes at the patch level-rather than at the point level or image level-to enable both accurate local boundary delineation and global shape coherence. Extensive evaluations on two tasks (2D polyp segmentation and 3D abdominal organ segmentation) show that SwIPE significantly improves over recent implicit approaches and outperforms state-of-the-art discrete methods with over 10x fewer parameters. Our method also demonstrates superior data efficiency and improved robustness to data shifts across image resolutions and datasets. Code is available on Github.

关键词： medical image Segmentation Deep Implicit Shape Representations Patch Embeddings Implicit Shape Regularization

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