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

共 2691 条记录，以下是481-490 订阅

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Online Easy Example Mining for Weakly-Supervised Gland Segmentation from Histology images 1

引用

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

作者： Li, Yi Yu, Yiduo Zou, Yiwen Xiang, Tianqi Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China HKUST Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis;however, the high cost of pixel-level annotations hinders its applications to broader diseases. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets. We observe that, unlike natural images, the key problem with histology images is the confusion of classes owning to morphological homogeneity and low color contrast among different tissues. To this end, we propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals rather than noisy signals, therefore mitigating the influence of inevitable false predictions in pseudo-masks. According to the characteristics of glandular datasets, we design a strong framework for gland segmentation. Our results exceed many fully-supervised methods and weakly-supervised methods for gland segmentation over 4.6% and 6.04% at mIoU, respectively. Code is available at https://github. com/xmed-lab/OEEM.

关键词： Online Easy Example Mining Histology image Gland segmentation Wealy-supervised semantic segmentation

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Transformer Based Multi-view Network for Mammographic image Classification 1

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

作者： Sun, Zizhao Jiang, Huiqin Ma, Ling Yu, Zhan Xu, Hongwei Zhengzhou Univ Zhengzhou Peoples R China Hosp Zhengzhou Univ Affiliated 1 Zhengzhou Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Most of the existing multi-view mammographic image analysis methods adopt a simple fusion strategy: features concatenation, which is widely used in many features fusion methods. However, concatenation based methods can't extract cross view information very effectively because different views are likely to be unaligned. Recently, many researchers have attempted to introduce attention mechanism related methods into the field of multi-view mammography analysis. But these attention mechanism based methods still partly rely on convolution, so they can't take full advantages of attention mechanism. To take full advantage of multi-view information, we propose a novel pure transformer based multi-view network to solve the question of mammographic image classification. In our primary network, we use a transformer based backbone network to extract image features, a "cross view attention block" structure to fuse multi-view information, and a "classification token" to gather all useful information to make the final prediction. Besides, we compare the performance when fusing multi-view information at different stages of the backbone network using a novel designed "(shifted) window based cross view attention block" structure and compare the results when fusing different views' information. the results on DDSM dataset show that our networks can effectively use multi-view information to make judgments and outperform the concatenation and convolution based methods.

关键词： Multi-view Mammographic image classification Transformer Cross view attention

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CACTUSS: Common Anatomical CT-US Space for US Examinations 1

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

作者： Velikova, Yordanka Simson, Walter Salehi, Mehrdad Azampour, Mohammad Farid Paprottka, Philipp Navab, Nassir Tech Univ Munich Comp Aided Med Procedures Munich Germany Klinikum Rechts Der Isar Intervent Radiol Munich Germany Sharif Univ Technol Dept Elect Engn Tehran Iran Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD USA

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and treatment planning. Recently, abdominal CT datasets have been successfully utilized to train deep neural networks for automatic aorta segmentation. Knowledge gathered from this solved task could therefore be leveraged to improve US segmentation for AAA diagnosis and monitoring. To this end, we propose CACTUSS: a common anatomical CT-US space, which acts as a virtual bridge between CT and US modalities to enable automatic AAA screening sonography. CACTUSS makes use of publicly available labelled data to learn to segment based on an intermediary representation that inherits properties from both US and CT. We train a segmentation network in this new representation and employ an additional image-to-image translation network which enables our model to perform on real B-mode images. Quantitative comparisons against fully supervised methods demonstrate the capabilities of CACTUSS in terms of Dice Score and diagnostic metrics, showing that our method also meets the clinical requirements for AAA scanning and diagnosis.

关键词： Ultrasound computer aided intervention Abdominal Aortic Aneurysm Domain Adaptation

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Usable Region Estimate for Assessing Practical Usability of medical image Segmentation Models 25th

Usable Region Estimate for Assessing Practical Usability of ...

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

作者： Zhang, Yizhe Mishra, Suraj Liang, Peixian Zheng, Hao Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031164439;9783031164422

We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted. We first propose a measure, Correctness-Confidence Rank Correlation (CCRC), to capture how predictions' confidence estimates correlate with their correctness scores in rank. A model with a high value of CCRC means its prediction confidences reliably suggest which samples' predictions are more likely to be correct. Since CCRC does not capture the actual prediction correctness, it alone is insufficient to indicate whether a prediction model is both accurate and reliable to use in practice. therefore, we further propose another method, Usable Region Estimate (URE), which simultaneously quantifies predictions' correctness and reliability of confidence assessments in one estimate. URE provides concrete information on to what extent a model's predictions are usable. In addition, the sizes of usable regions (UR) can be utilized to compare models: A model with a larger UR can be taken as a more usable and hence better model. Experiments on six datasets validate that the proposed evaluation methods perform well, providing a concrete and concise measure for the practical usability of medical image segmentation models.

关键词： medical AI models Model evaluation Practical usability Unified measure medical image segmentation

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Dynamic Bank Learning for Semi-supervised Federated image Diagnosis with Class Imbalance 1

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

作者： Jiang, Meirui Yang, Hongzheng Li, Xiaoxiao Liu, Quande Heng, Pheng-Ann Dou, Qi Chinese Univ Hong Kong Dept Comp Sci & Engn Sha Tin Hong Kong Peoples R China Beihang Univ Dept Artificial Intelligence Beijing Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In this paper, we study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi), which allows all clients to have only unlabeled data while the server just has a small amount of labeled data. this imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. this scheme consists of two parts, i.e., the dynamic bank construction to distill various class proportions for each local client, and the sub-bank classification to impose the local model to learn different class proportions. We evaluate our approach on two public real-world medical datasets, including the intracranial hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with 10,015 dermoscopy images. the effectiveness of our method has been validated with significant performance improvements (7.61% and 4.69%) compared with the second-best on the accuracy, as well as comprehensive analytical studies.

关键词： Semi-supervised FL Class imbalance image diagnosis

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Multi-modal Retinal image Registration Using a Keypoint-Based Vessel Structure Aligning Network 25th

Multi-modal Retinal Image Registration Using a Keypoint-Base...

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

作者： Sindel, Aline Hohberger, Bettina Maier, Andreas Christlein, Vincent FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Univ Klinikum Erlangen Dept Ophthalmol Erlangen Germany

ISBN: (纸本)9783031164460;9783031164453

In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel structure for keypoint detection and description and uses a graph neural network for feature matching. the keypoint detection and description network and graph neural network are jointly trained in a self-supervised manner using synthetic multi-modal image pairs and are guided by synthetically sampled ground truth homographies. Our method demonstrates higher registration accuracy as competing methods for our synthetic retinal dataset and generalizes well for our real macula dataset and a public fundus dataset.

关键词： Multi-modal retinal image registration Convolutional neural networks Graph neural networks

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Test-Time Adaptation with Calibration of medical image Classification Nets for Label Distribution Shift 1

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

作者： Ma, Wenao Chen, Cheng Zheng, Shuang Qin, Jing Zhang, Huimao Dou, Qi Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China First Hosp Jilin Univ Dept Radiol Changchun Peoples R China Jilin Prov Key Lab Med Imaging Big Data Changchun Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Kowloon Hong Kong Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. Our approach innovates distribution calibration to learn multiple representative classifiers, which are capable of handling different one-dominating-class distributions. When given a test image, the diverse classifiers are dynamically aggregated via the consistency-driven test-time adaptation, to deal with the unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis staging and COVID-19 severity prediction. Our experiments clearly show the decreased model performance under label shift. With our method, model performance significantly improves on all the test datasets with different label shifts for both medical image diagnosis tasks.

关键词： Test-time adaptation Label distribution shift medical image classification

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Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR images 1st

Progressive Multi-channel Fusion Network for Myocardial Pat...

引用

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： Wang, Jingyan Gong, Xiaojuan Jin, Tangruoyi Gao, Ruikun Zheng, Shaohua Chen, Weisheng College of Physics and Information Engineering Fuzhou University Fuzhou350108 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Thoracic Surgery Clinical Oncology School of Fujian Medical University Fujian Cancer Hospital FJ Fuzhou350108 China

ISBN: (纸本)9783031870088

Cardiac scarring and edema are critical pathological features of heart diseases. Accurate segmentation of these features in Cardiac Magnetic Resonance (CMR) imaging is crucial for understanding the pathological changes associated with cardiac diseases. In the field of myocardial scar and edema segmentation, it is of significant importance to study the C0, T2, and LGE modalities. these modalities offer different perspectives on myocardial tissue characteristics, aiding in the more accurate diagnosis and assessment of cardiac diseases. However, the high-intensity features of scars and edema cannot be directly obtained from individual CMR imaging sequences, making simultaneous accurate segmentation challenging. To address this, we propose a multi-modal, multi-channel fusion interactive progressive segmentation strategy that leverages the distinctive properties of each modality and the surrounding tissue characteristics for the segmentation of myocardial scars and edema. We have designed a multi-channel fusion interactive progressive segmentation model, suitable for scar and myocardial segmentation, which incorporates an attention mechanism that enhances channel information interaction within a U-Net structure to extract features across different modalities. On the MyoPS++ 2024 public dataset, our method achieved an average Dice score of 0.5486 for scar segmentation and 0.6081 for the segmentation of both scars and edema. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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vMFNet: Compositionality Meets Domain-Generalised Segmentation 25th

vMFNet: Compositionality Meets Domain-Generalised Segmentati...

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

作者： Liu, Xiao thermos, Spyridon Sanchez, Pedro O'Neil, Alison Q. Tsaftaris, Sotirios A. Univ Edinburgh Sch Engn Edinburgh EH9 3FB Midlothian Scotland AC Codewheel Ltd Larnax Cyprus Alan Turing Inst London England Canon Med Res Europe Ltd Edinburgh Midlothian Scotland

ISBN: (纸本)9783031164491;9783031164484

Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from medical (e.g. MRI and CT) images with some limited guidance. Such recognition ability can easily generalise to new images from different clinical centres. this rapid and generalisable learning ability is mostly due to the compositional structure of image patterns in the human brain, which is less incorporated in medical image segmentation. In this paper, we model the compositional components (i.e. patterns) of human anatomy as learnable von-Mises-Fisher (vMF) kernels, which are robust to images collected from different domains (e.g. clinical centres). the image features can be decomposed to (or composed by) the components with the composing operations, i.e. the vMF likelihoods. the vMF likelihoods tell how likely each anatomical part is at each position of the image. Hence, the segmentation mask can be predicted based on the vMF likelihoods. Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image. Extensive experiments show that the proposed vMFNet achieves improved generalisation performance on two benchmarks, especially when annotations are limited. Code is publicly available at: https://***/vios-s/vMFNet.

关键词： Compositionality Domain generalisation Semi-supervised learning Test-time training medical image segmentation

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intervention & Interaction Federated Abnormality Detection with Noisy Clients 25th

Intervention & Interaction Federated Abnormality Detection w...

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

作者： Liu, Xinyu Li, Wuyang Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Federated learning (FL), which trains a shared global model by collaboration between distributed clients (e.g. medical institutions) and preserves the privacy of local data, has been widely deployed in the medical field to benefit abnormality diagnosis. However, it is inevitable that local data contains noise across clients, resulting in notably performance deterioration in the global model. To this end, a practical yet challenging FL problem is studied in this paper, namely Federated abnormality detection with noisy clients (FADN). We represent the first effort to reason the FADN task as a structural causal model, and identify the main issue that leads to the performance deterioration, namely recognition bias. To tackle the problem, an intervention & Interaction FL framework (FedInI) is proposed, comprising two key strategies: (1) intervention: considering the data distribution heterogeneity caused by different noisy levels within each client, we use the global model to intervene the training of local models, by shuffling and mixing features extracted from different models and suppress the noise gradually;(2) Interaction: we devise an adaptive sample-wise weighting strategy that jointly considers the local training statuses and global noisy levels with a shared interactive layer. Extensive experiments on class-conditional noise and instance-dependant noise settings are conducted, FedInI outperforms state-of-the-arts by a remarkable margin. Code is available at github. corn/CityU-AIM-Group/FedInl.

关键词： Federated learning Learning with noisy labels Abnormality detection Causal intervention

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