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

共 2882 条记录，以下是491-500 订阅

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A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000 25th

A Comprehensive Study of Modern Architectures and Regulariza...

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

作者： Ihler, Sontje Kuhnke, Felix Spindeldreier, Svenja Leibniz Univ Hannover Inst Mech Syst Hannover Germany Leibniz Univ Hannover Inst Informat Proc Hannover Germany

ISBN: (纸本)9783031164316;9783031164309

computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these datasets are still small compared to natural image datasets like imageNet1k, imageNet21k and JFT, they are large for annotated medical datasets, where 1k-10k labeled samples are much more common. there is no baseline on which methods to build on in the low data regime. In this work we bridge this gap by providing an extensive study on medical image classification with limited annotations (5k). We present a study of modern architectures applied to a fixed low data regime of 5000 images on the CheXpert dataset. Conclusively we find that models pretrained on imageNet21k achieve a higher AUC and larger models require less training steps. All models are quite well calibrated even though we only fine-tuned on 5000 training samples. All 'modern' architectures have higher AUC than ResNet50. Regularization of Big Transfer Models with MixUp or Mean Teacher improves calibration, MixUp also improves accuracy. Vision Transformer achieve comparable or on par results to Big Transfer Models.

关键词： medical image classification Limited data Transfer learning

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Synthesising Brain Iron Maps from Quantitative Magnetic Resonance images Using Interpretable Generative Adversarial Networks 26th

Synthesising Brain Iron Maps from Quantitative Magnetic Reso...

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

作者： Munroe, Lindsay Deprez, Maria Michaelides, Christos Parkes, Harry G. Geraki, Kalotina Herlihy, Amy H. So, Po-Wah Kings Coll London Dept Neuroimaging Inst Psychiat Psychol & Neurosci London England Kings Coll London Sch Biomed Engn & Imaging Sci London England Diamond Light Source Harwell Sci & Innovat Campus Didcot Oxon England Perspectum Diagnost Gemini One5520 John Smith Dr Oxford England

ISBN: (纸本)9783031474248;9783031474255

Accurate spatial estimation of brain iron concentration in-vivo is vital to elucidate the role of iron in neurodegenerative diseases, among other applications. However, ground truth quantitative iron maps of the brain can only be acquired post-mortem from ex-vivo samples. Quantitative magnetic resonance imaging (QMRI) methods are iron-sensitive and hold potential to quantitatively measure brain iron. We hypothesise interpretability methods can identify the most salient QMRI parameter(s) for iron prediction. In this study, a generative adversarial network with spatially adaptive normalisation layers (SPADE) was trained to synthesise maps of brain iron content from QMRI parameters, including those from relaxometry, diffusion and magnetisation transfer MRI. Ground truth maps of iron content were obtained by synchrotron radiation X-ray fluorescence (SRXRF). QMRI and SRXRF datasets were registered, and a distribution-based loss was proposed to address misalignment from multi-modal QMRI-to-SRXRF registration. To enable interpretation, channel attention was incorporated to learn feature importance for QMRI parameters. Attention weights were compared against occlusion and local interpretable model-agnostic explanations. Our model achieved dice scores of 0.97 and 0.95 for grey and white matter, respectively, when comparing tissue boundaries of synthesised vs. MRI images. Examining the contrast in predicted vs. ground truth iron maps, our model achieved 15.2% and 17.8% normalised absolute error for grey and white matter, respectively. All three interpretable methods ranked fractional anisotropy as the most salient, followed by myelin water fraction and magnetisation transfer ratio. the co-location of iron and myelin may explain the finding that myelin-related QMRI parameters are strong predictors of iron.

关键词： Supervised image-to-image translation Interpretable deep learning Iron prediction

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Enhanced Camouflage Detection Using Advanced image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

the identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. this finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. this dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. this research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. the LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. the approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. the recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy image processing Surveillance image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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What Can We Learn About a Generated image Corrupting Its Latent Representation? 25th

What Can We Learn About a Generated Image Corrupting Its Lat...

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

作者： Tomczak, Agnieszka Gupta, Aarushi Ilic, Slobodan Navab, Nassir Albarqouni, Shadi Tech Univ Munich Fac Informat Munich Germany Siemens AG Munich Germany Indian Inst Technol Delhi Helmholtz New Delhi India Univ Hosp Bonn Clin Diagnost & Intervent Radiol Bonn Germany Helmholz Zentrum Munich Helmholtz AI Munich Germany

ISBN: (纸本)9783031164460;9783031164453

Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. they can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effectiveness in learning the underlying data distribution, it can lead to a discrepancy between input and output data. the purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck. We achieve this by corrupting the latent representation with noise and generating multiple outputs. the degree of differences between them is interpreted as the strength of the representation: the more robust the latent representation, the fewer changes in the output image the corruption causes. Our results demonstrate that our proposed method has the ability to i) predict uncertain parts of synthesized images, and ii) identify samples that may not be reliable for downstream tasks, e.g., liver segmentation task.

关键词： GANs image synthesis Uncertainty image quality

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An Efficient Defending Mechanism Against image Attacking on medical image Segmentation Models 1

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1st international Workshop on Resource-Efficient medical image Analysis (REMIA) / 25th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Le, Linh D. Fu, Huazhu Xu, Xinxing Liu, Yong Xu, Yanyu Du, Jiawei Zhou, Joey T. Goh, Rick ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (数字)9783031168765

ISBN: (纸本)9783031168765;9783031168758

image attacking has been studied for a long time. However, in reality, the number of research on defending against the attacks on segmentation models is still limited especially for medical imaging. To fill this research gap, we propose a novel defending mechanism against adversarial attacks for the segmentation models. We focus on segmentation as robustness improvement on segmentation is much more challenging due to its dense nature, and segmentation is at the center of medical imaging tasks. In this paper, we are the first time to employ Transformer as a technique to protect the segmentation models from attacks. Our result on several medical well-known benchmark datasets shows that the proposed defending mechanism to enhance the segmentation models is effective with high scores and better compared to other strong methods.

关键词： Defending Adversarial attacking medical image segmentation Transformer

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Self-Ensembling Vision Transformer (SEViT) for Robust medical image Classification 1

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

作者： Almalik, Faris Yaqub, Mohammad Nandakumar, Karthik Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging such as classification and segmentation. While the vulnerability of CNNs to adversarial attacks is a well-known problem, recent works have shown that ViTs are also susceptible to such attacks and suffer significant performance degradation under attack. the vulnerability of ViTs to carefully engineered adversarial samples raises serious concerns about their safety in clinical settings. In this paper, we propose a novel self-ensembling method to enhance the robustness of ViT in the presence of adversarial attacks. the proposed Self-Ensembling Vision Transformer (SEViT) leverages the fact that feature representations learned by initial blocks of a ViT are relatively unaffected by adversarial perturbations. Learning multiple classifiers based on these intermediate feature representations and combining these predictions with that of the final ViT classifier can provide robustness against adversarial attacks. Measuring the consistency between the various predictions can also help detect adversarial samples. Experiments on two modalities (chest X-ray and fundoscopy) demonstrate the efficacy of SEViT architecture to defend against various adversarial attacks in the gray-box (attacker has full knowledge of the target model, but not the defense mechanism) setting. Code: https://***/faresmalik/SEViT

关键词： Adversarial attack Vision transformer Self-ensemble

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RPTK: the Role of Feature Computation on Prediction Performance 26th

RPTK: The Role of Feature Computation on Prediction Performa...

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

作者： Bohn, Jonas R. Heidt, Christian M. Almeida, Silvia D. Kausch, Lisa Goetz, Michael Nolden, Marco Christopoulos, Petros Rheinheimer, Stephan Peters, Alan A. von Stackelberg, Oyunbileg Kauczor, Hans-Ulrich Maier-Hein, Klaus H. Heussel, Claus P. Norajitra, Tobias German Canc Res Ctr Div Med Image Comp Heidelberg Germany German Ctr Lung Res DZL Translat Lung Res Ctr TLRC Heidelberg Germany Heidelberg Univ Fac Biosci Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany Univ Hosp Heidelberg Diagnost & Intervent Radiol Heidelberg Germany Heidelberg Univ Fac Med Heidelberg Germany German Canc Res Ctr AI Hlth Innovat Cluster Heidelberg Germany Univ Ulm Med Ctr Expt Radiol Ulm Germany Univ Hosp Heidelberg Pattern Anal & Learning Grp Heidelberg Germany Thoraxklinik Thorac Oncol Heidelberg Germany Thoraxklinik Diagnost & Intervent Radiol Nucl Med Heidelberg Germany Univ Bern Dept Diagnost Intervent & Pediat Radiol Univ Hosp Bern Bern Switzerland

ISBN: (纸本)9783031474248;9783031474255

A rise of radiomics studies and techniques could be observed over the past few years, which centers around the extraction and analysis of quantitative features from medical images. Radiomics offers numerous advantages in disease characterization and treatment response prediction. Despite its promise, radiomics faces challenges in standardizing features and techniques, leading to large variations of approaches across studies and centers, making it difficult to determine the most suitable techniques for any given clinical scenario. Additionally, manually constructing optimized radiomics pipelines can be time-consuming. Recent works (WORC, Autoradiomics) have addressed the aforementioned shortcomings by introducing radiomics-based frameworks for automated pipeline optimization. Both approaches comprehensively span the entire radiomics workflow, enabling consistent, comprehensive, and reproducible radiomics analyses. In contrast, finding the ideal solutions for the workflow's feature extractor and feature selection components, has received less attention. To address this, we propose the Radiomics Processing Toolkit (RPTK), which adds comprehensive feature extraction and selection components from PyRadiomics and from the medical image Radiomics Processor (MIRP) to the radiomics automation pipeline. To validate our approach and demonstrate benefits from the feature-centered components, we comprehensively compared RPTK with results from WORC and Autoradiomics on six public benchmark data sets. We show that we can achieve higher performance by incorporating the proposed feature processing and selection techniques. Our results provide additional guidance in selecting suitable components for optimized radiomics analyses in clinical use cases such as treatment response prediction.

关键词： Radiomics Evaluation Automated Radiomics Processing Radiomics Processing Toolkit

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PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound images 26th

PLD-AL: Pseudo-label Divergence-Based Active Learning in Car...

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

作者： Tang, Yucheng Hu, Yipeng Li, Jing Lin, Hu Xu, Xiang Huang, Ke Lin, Hongxiang Zhejiang Univ Sch Math Sci Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China UCL Ctr Med Image Comp & Wellcome EPSRC Ctr Intervent & Surg Sci London England Zhejiang Univ Childrens Hosp Dept Endocrinol Sch Med Hangzhou Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Segmentation of the carotid intima-media (CIM) offers more precise morphological evidence for obesity and atherosclerotic disease compared to the method that measures its thickness and roughness during routine ultrasound scans. Although advanced deep learning technology has shown promise in enabling automatic and accurate medical image segmentation, the lack of a large quantity of high-quality CIM labels may hinder the model training process. Active learning (AL) tackles this issue by iteratively annotating the subset whose labels contribute the most to the training performance at each iteration. However, this approach substantially relies on the expert's experience, particularly when addressing ambiguous CIM boundaries that may be present in real-world ultrasound images. Our proposed approach, called pseudo-label divergence-based active learning (PLD-AL), aims to train segmentation models using a gradually enlarged and refined labeled pool. the approach has an outer and an inner loops: the outer loop calculates the Kullback-Leibler (KL) divergence of predictive pseudo-labels related to two consecutive AL iterations. It determines which portion of the unlabeled pool should be annotated by an expert. the inner loop trains two networks: the student network is fully trained on the current labeled pool, while the teacher network is weighted upon itself and the student one, ultimately refining the labeled pool. We evaluated our approach using both the Carotid Ultrasound Boundary Study dataset and an in-house dataset from Children's Hospital, Zhejiang University School of Medicine. Our results demonstrate that our approach outperforms state-of-the-art AL approaches. Furthermore, the visualization results show that our approach less overestimates the CIM area than the rest methods, especially for severely ambiguous ultrasound images at the thickness direction.

关键词： Carotid intima-media complex active learning image segmentation

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Unsupervised Deformable image Registration with Absent Correspondences in Pre-operative and Post-recurrence Brain Tumor MRI Scans 25th

Unsupervised Deformable Image Registration with Absent Corre...

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

作者： Mok, Tony C. W. Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment. While recent deep learning-based deformable registration methods have achieved remarkable success with healthy brain images, most of them would be unable to accurately align images with pathologies due to the absent correspondences in the reference image. In this paper, we propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. A forward-backward consistency constraint is used to aid in the localization of the resection and recurrence region from voxels with absence correspondences in the two images. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment compared to traditional and deep learning-based registration approaches with or without cost function masking strategy. the source code is available at https://***/cwmok/DIRAC.

关键词： Absent correspondences Patient-specific registration Deformable registration

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A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound images 1

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

作者： Liu, Ting An, Xing Liu, Yanbo Liu, Yuxi Lin, Bin Jiang, Runzhou Xu, Wenlong Cong, Longfei Zhu, Lei Shenzhen Mindray BioMed Elect Co Ltd Shenzhen Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

this paper presents a novel deep learning system to classify breast lesions in ultrasound images into benign and malignant and into Breast Imaging Reporting and Data System (BI-RADS) six categories simultaneously. A multitask soft label generating architecture is proposed to improve the classification performance, in which task-correlated labels are obtained from a dual-task teacher network and utilized to guide the training of a student model. In student model, a consistency supervision mechanism is embedded to constrain that a prediction of BI-RADS is consistent with the predicted pathology result. Moreover, a cross-class loss function that penalizes different degrees of misclassified items with different weights is introduced to make the prediction of BI-RADS closer to the annotation. Experiments on our private and two public datasets show that the proposed system outperforms current state-of-the-art methods, demonstrating the great potential of our method in clinical diagnosis.

关键词： Classification Breast ultrasound image Multitask soft label Consistency supervision Cross-class loss

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