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

共 2694 条记录，以下是251-260 订阅

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Recruiting the Best Teacher Modality: A Customized Knowledge Distillation Method for if Based Nephropathy Diagnosis 26th

Recruiting the Best Teacher Modality: A Customized Knowledge...

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

作者： Dai, Ning Jiang, Lai Fu, Yibing Pan, Sai Xu, Mai Deng, Xin Chen, Pu Chen, Xiangmei Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Sch Chinese PLA Natl Clin Res Ctr Kidney DisDept Nephrol State Key Lab Kidney DisInst Nephrol Chinese PLA Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China

ISBN: (纸本)9783031439032;9783031439049

the joint use of multiple imaging modalities for medical image has been widely studied in recent years. the fusion of information from different modalities has demonstrated the performance improvement for some medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used medical image due to its ease of acquisition with low cost, which is also an advanced multi-modality technique. However, the existing methods mainly integrate information from diverse sources by averaging or combining them, failing to exploit multi-modality knowledge in details. In this paper, we observe that the 7 modalities of IF images have different impact on different nephropathy categories. Accordingly, we propose a knowledge distillation framework to transfer knowledge from the trained single-modality teacher networks to a multi-modality student network. On top of this, given a input IF sequence, a recruitment module is developed to dynamically assign weights to teacher models and optimize the performance of student model. By applying on several different architectures, the extensive experimental results verify the effectiveness of our method for nephropathy diagnosis.

关键词： Nephropathy diagnosis IF image Knowledge distillation

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RESToring Clarity: Unpaired Retina image Enhancement Using Scattering Transform 26th

RESToring Clarity: Unpaired Retina Image Enhancement Using S...

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

作者： Oh, Ellen Jieun Hwang, Yechan Han, Yubin Choi, Taegeun Lee, Geunyoung Kim, Won Hwa Pohang Univ Sci & Technol POSTECH Pohang South Korea Mediwhale Seoul South Korea

ISBN: (纸本)9783031439988;9783031439995

Retina images are non-invasive and highly effective in the diagnosis of various diseases such as cardiovascular and ophthalmological diseases. Accurate diagnosis depends on the quality of the retina images, however, obtaining high-quality images can be challenging due to various factors, such as noise, artifacts, and eyemovement. Methods for enhancing retina images are therefore in high demand for clinical purposes, yet the problem remains challenging as there is a natural trade-off between preserving anatomical details (e.g., vessels) and increasing overall image quality other than the content in it. Moreover, training an enhancement model often requires paired images that map low-quality images to high-quality images, which may not be available in practice. In this regime, we propose a novel Retina image Enhancement framework using ScatteringTransform (REST). REST uses unpaired retina image sets and does not require prior knowledge of the degraded factors. the generator in REST enhances retina images by utilizing the Anatomy Preserving Branch (APB) and the Tone Transferring Branch (TTB) with different roles. Our model successfully enhances low-quality retina images demonstrating commendable results on two independent datasets.

关键词： image enhancement

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OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology image Classification 26th

OpenAL: An Efficient Deep Active Learning Framework for Open...

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

作者： Qu, Linhao Ma, Yingfan Yang, Zhiwei Wang, Manning Song, Zhijian Fudan Univ Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China Shanghai Key Lab Med Image Comp & Comp Assisted I Shanghai 200032 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Active learning (AL) is an effective approach to select the most informative samples to label so as to reduce the annotation cost. Existing AL methods typically work under the closed-set assumption, i.e., all classes existing in the unlabeled sample pool need to be classified by the target model. However, in some practical clinical tasks, the unlabeled pool may contain not only the target classes that need to be fine-grainedly classified, but also non-target classes that are irrelevant to the clinical tasks. Existing AL methods cannot work well in this scenario because they tend to select a large number of non-target samples. In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples. Experiments on fine-grained classification of pathology images show that OpenAL can significantly improve the query quality of target class samples and achieve higher performance than current state-of-the-art AL methods. Code is available at https:// ***/miccaiif/OpenAL.

关键词： Active learning Openset Pathology image classification

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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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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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TCL: Triplet Consistent Learning for Odometry Estimation of Monocular Endoscope 26th

TCL: Triplet Consistent Learning for Odometry Estimation of ...

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

作者： Yue, Hao Gu, Yun Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China

ISBN: (纸本)9783031439957;9783031439964

the depth and pose estimations from monocular images are essential for computer-aided navigation. Since the ground truth of depth and pose are difficult to obtain, the unsupervised training method has a broad prospect in endoscopic scenes. However, endoscopic datasets lack sufficient diversity of visual variations, and appearance inconsistency is also frequently observed in image triplets. In this paper, we propose a triplet-consistency-learning framework (TCL) consisting of two modules: Geometric Consistency module(GC) and Appearance Inconsistency module(AiC). To enrich the diversity of endoscopic datasets, the GC module generates synthesis triplets and enforces geometric consistency via specific losses. To reduce the appearance inconsistency in the image triplets, the AiC module introduces a tripletmasking strategy to act on photometric loss. TCL can be easily embedded into various unsupervised methods without adding extra model parameters. Experiments on public datasets demonstrate that TCL effectively improves the accuracy of unsupervised methods even with limited number of training samples. Code is available at https://***/ EndoluminalSurgicalVision-IMR/TCL.

关键词： Self-supervised monocular pose estimation Endoscopic images Data augmentation Appearance inconsistency

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How Reliable are the Metrics Used for Assessing Reliability in medical Imaging? 1

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

作者： Gupta, Mayank Basu, Soumen Arora, Chetan Indian Inst Technol Delhi India

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Deep Neural Networks (DNNs) have been successful in various computer vision tasks, but are known to be uncalibrated, and make overconfident mistakes. this erodes a user's confidence in the model and is a major concern in their applicability for critical tasks like medical imaging. In the last few years, researchers have proposed various metrics to measure miscalibration, and techniques to calibrate DNNs. However, our investigation shows that for small datasets, typical for medical imaging tasks, the common metrics for calibration, have a large bias as well as variance. It makes these metrics highly unreliable, and unusable for medical imaging. Similarly, we show that state-of-the-art (SOTA) calibration techniques while effective on large natural image datasets, are ineffective on small medical imaging datasets. We discover that the reason for failure is large variance in the density estimation using a small sample set. We propose a novel evaluation metric that incorporates the inherent uncertainty in the predicted confidence, and regularizes the density estimation using a parametric prior model. We call our metric, Robust Expected Calibration Error (RECE), which gives a low bias, and low variance estimate of the expected calibration error, even on the small datasets. In addition, we propose a novel auxiliary loss - Robust Calibration Regularization (RCR) which rectifies the above issues to calibrate the model at train time. We demonstrate the effectiveness of our RECE metric as well as the RCR loss on several medical imaging datasets and achieve SOTA calibration results on both standard calibration metrics as well as RECE. We also show the benefits of using our loss on general classification datasets. the source code and all trained models have been released (https://***/MayankGupta73/Robust-Calibration).

关键词： Confidence Calibration Uncertainty Estimation image Classification

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Enhancing Foundation Model Robustness for Multi-center Real-World medical image Analysis 1st

Enhancing Foundation Model Robustness for Multi-center Real...

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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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Shape-Guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature 3rd

Shape-Guided Conditional Latent Diffusion Models for Synthes...

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Deo, Yash Dou, Haoran Ravikumar, Nishant Frangi, Alejandro F. Lassila, Toni Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Sch Comp Leeds England Univ Leeds Sch Med Leeds England NIHR Leeds Biomed Res Ctr BRC Leeds England Alan Turing Inst London England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Elect Engn Dept Leuven Belgium Katholieke Univ Leuven Med Imaging Res Ctr MIRC Cardiovasc Sci Dept Leuven Belgium Univ Manchester Div Informat Imaging & Data Sci Sch Comp Sci Manchester England Univ Manchester Div Informat Imaging & Data Sci Sch Hlth Sci Manchester England

ISBN: (纸本)9783031537660;9783031537677

the Circle ofWillis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases and refine clinical interventions. However, comprehensive investigation of less prevalent CoW variations remains challenging because of the dominance of a few commonly occurring configurations. We propose a novel generative approach utilising a conditional latent diffusion model with shape and anatomical guidance to generate realistic 3D CoW segmentations, including different phenotypical variations. Our conditional latent diffusion model incorporates shape guidance to better preserve vessel continuity and demonstrates superior performance when compared to alternative generative models, including conditional variants of 3D GAN and 3D VAE. We observed that our model generated CoW variants that are more realistic and demonstrate higher visual fidelity than competing approaches with an FID score 53% better than the best-performing GAN-based model.

关键词： image Synthesis Deep Learning Brain Vasculature Vessel Synthesis Diffusion Latent Diffusion

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PET image Denoising with Score-Based Diffusion Probabilistic Models 26th

PET Image Denoising with Score-Based Diffusion Probabilistic...

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

作者： Shen, Chenyu Yang, Ziyuan Zhang, Yi Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Low-count positron emission tomography (PET) imaging is an effective way to reduce the radiation risk of PET at the cost of a low signal-to-noise ratio. Our study aims to denoise low-count PET images in an unsupervised mode since the mainstream methods usually rely on paired data, which is not always feasible in clinical practice. We adopt the diffusion probabilistic model in consideration of its strong generation ability. Our model consists of two stages. In the training stage, we learn a score function network via evidence lower bound (ELBO) optimization. In the sampling stage, the trained score function and low-count image are employed to generate the corresponding high-count image under two handcrafted conditions. One is based on restoration in latent space, and the other is based on noise insertion in latent space. thus, our model is named the bidirectional condition diffusion probabilistic model (BC-DPM). Real patient whole-body data are utilized to evaluate our model. the experiments show that our model achieves better performance in both qualitative and quantitative aspects compared to several traditional and recently proposed learning-based methods.

关键词： PET denoising diffusion probabilistic model latent space conditions

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