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

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BMD-GAN: Bone Mineral Density Estimation Using X-Ray image Decomposition into Projections of Bone-Segmented Quantitative Computed Tomography Using Hierarchical Learning 25th

BMD-GAN: Bone Mineral Density Estimation Using X-Ray Image D...

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

作者： Gu, Yi Otake, Yoshito Uemura, Keisuke Soufi, Mazen Takao, Masaki Sugano, Nobuhiko Sato, Yoshinobu Nara Inst Sci & Technol Grad Sch Sci & Technol Div Informat Sci Ikoma Japan Osaka Univ Dept Orthopaed Grad Sch Med Suita Japan Ehime Univ Dept Bone & Joint Surg Grad Sch Med Matsuyama Japan Osaka Univ Dept Orthopaed Med Engn Grad Sch Med Suita Japan

ISBN: (纸本)9783031164460;9783031164453

We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis;however, these modalities require special equipment and scan protocols. Measuring BMD from an x-ray image provides an opportunistic screening, which is potentially useful for early diagnosis. the previous methods that directly learn the relationship between x-ray images and BMD require a large training dataset to achieve high accuracy because of large intensity variations in the x-ray images. therefore, we propose an approach using the QCT for training a generative adversarial network (GAN) and decomposing an x-ray image into a projection of bone-segmented QCT. the proposed hierarchical learning improved the robustness and accuracy of quantitatively decomposing a small-area target. the evaluation of 200 patients with osteoarthritis using the proposed method, which we named BMD-GAN, demonstrated a Pearson correlation coefficient of 0.888 between the predicted and ground truth DXA-measured BMD. Besides not requiring a large-scale training database, another advantage of our method is its extensibility to other anatomical areas, such as the vertebrae and rib bones.

关键词： Generative adversarial network (GAN) Radiography Bone mineral density (BMD)

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Accurate and Explainable image-Based Prediction Using a Lightweight Generative Model 25th

Accurate and Explainable Image-Based Prediction Using a Ligh...

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

作者： Mauri, Chiara Cerri, Stefano Puonti, Oula Muehlau, Mark Van Leemput, Koen Tech Univ Denmark Dept Hlth Technol Lyngby Denmark Harvard Med Sch Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Charlestown MA USA Copenhagen Univ Hosp Hvidovre Danish Res Ctr Magnet Resonance Ctr Funct & Diagnost Imaging & Res Hvidovre Denmark Tech Univ Munich Dept Neurol Munich Germany Tech Univ Munich Sch Med TUM Neuroimaging Ctr Munich Germany

ISBN: (纸本)9783031164521;9783031164514

Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject's age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical alternatives that are easier to tune, train and interpret. the models we propose consist of (1) a causal forward model expressing the effect of variables of interest on brain morphology, and (2) a latent variable noise model, based on factor analysis, that is quick to learn and invert. In experiments estimating individuals' age and gender from the UK Biobank dataset, we demonstrate competitive prediction performance even when the number of training subjects is in the thousands - the typical scenario in many potential applications. the method is easy to use as it has only a single hyperparameter, and directly estimates interpretable spatial maps of the underlying structural changes that are driving the predictions.

关键词： Forecasting

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Unsupervised Contrastive Learning of image Representations from Ultrasound Videos with Hard Negative Mining 1

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

作者： Basu, Soumen Singla, Somanshu Gupta, Mayank Rana, Pratyaksha Gupta, Pankaj Arora, Chetan Indian Inst Technol Delhi India Postgrad Inst Med Educ & Res Chandigarh India

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive learning techniques consider frames within a video as positives in the embedding space, whereas the frames from other videos are considered negatives. We observe that unlike multiple views of an object in natural scene videos, an Ultrasound (US) video captures different 2D slices of an organ. Hence, there is almost no similarity between the temporally distant frames of even the same US video. In this paper we propose to instead utilize such frames as hard negatives. We advocate mining both intra-video and cross-video negatives in a hardness-sensitive negative mining curriculum in a UCL framework to learn rich image representations. We deploy our framework to learn the representations of Gallbladder (GB) malignancy from US videos. We also construct the first large-scale US video dataset containing 64 videos and 15,800 frames for learning GB representations. We show that the standard ResNet50 backbone trained with our framework improves the accuracy of models pretrained with SOTA UCL techniques as well as supervised pretrained models on imageNet for the GB malignancy detection task by 2-6%. We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1.5% compared to SOTA. Source code, dataset, and models are available at https://***/usucl.

关键词： Contrastive learning Ultrasound Negative mining

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Test Time Transform Prediction for Open Set Histopathological image Recognition 25th

Test Time Transform Prediction for Open Set Histopathologica...

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

作者： Galdran, Adrian Hewitt, Katherine J. Laleh, Narmin Ghaffari Kather, Jakob N. Carneiro, Gustavo Gonzalez Ballester, Miguel A. Univ Pompeu Fabra Dept Informat & Commun Technol BCN Medtech Barcelona Spain Univ Hosp RWTH Aachen Dept Med 3 Aachen Germany Univ Adelaide Adelaide SA Australia Catalan Inst Res & Adv Studies ICREA Barcelona Spain

ISBN: (纸本)9783031164347;9783031164330

Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to address this problem by applying Open Set Recognition techniques to the task of jointly classifying tissue that belongs to a set of annotated classes, e.g. clinically relevant tissue categories, while rejecting in test time Open Set samples, i.e. images that belong to categories not present in the training set. To this end, we introduce a new approach for Open Set histopathological image recognition based on training a model to accurately identify image categories and simultaneously predict which data augmentation transform has been applied. In test time, we measure model confidence in predicting this transform, which we expect to be lower for images in the Open Set. We carry out comprehensive experiments in the context of colorectal cancer assessment from histological images, which provide evidence on the strengths of our approach to automatically identify samples from unknown categories. Code is released at https://***/agaldran/t3po.

关键词： Histopathological image analysis Open Set Recognition

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Denoising for Relaxing: Unsupervised Domain Adaptive Fundus image Segmentation Without Source Data 25th

Denoising for Relaxing: Unsupervised Domain Adaptive Fundus ...

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

作者： Xu, Zhe Lu, Donghuan Wang, Yixin Luo, Jie Wei, Dong Zheng, Yefeng Tong, Raymond Kai-Yu Chinese Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China Tencent Healthcare Co Jarvis Lab Shenzhen Peoples R China Stanford Univ Dept Bioengn Stanford CA USA Harvard Med Sch Brigham & Womens Hosp Boston MA USA

ISBN: (纸本)9783031164439;9783031164422

Recently, unsupervised domain adaptation (UDA) has been actively explored for multi-site fundus image segmentation with domain discrepancy. Despite relaxing the requirement of target labels, typical UDA still requires the labeled source data to achieve distribution alignment during adaptation. Unfortunately, due to privacy concerns, the vendor side often cannot provide the source data to the targeted client side in clinical practice, making the adaptation more challenging. To address this, in this work, we present a novel uncertainty-rectified denoising-for-relaxing (U-D4R) framework, aiming at completely relaxing the source data and effectively adapting the pretrained source model to the target domain. Considering the unreliable source model predictions on the target domain, we first present an adaptive class-dependent threshold strategy as the coarse denoising process to generate the pseudo labels. then, the uncertainty-rectified label soft correction is introduced for fine denoising by taking advantage of estimating the joint distribution matrix between the observed and latent labels. Extensive experiments on cross-domain fundus image segmentation showed that our approach significantly outperforms the state-of-the-art source-free methods and encouragingly achieves comparable or even better performances over the leading source-dependent methods.

关键词： Domain adaptation Label denoising Fundus image

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Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types 25th

Identifying Phenotypic Concepts Discriminating Molecular Bre...

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

作者： Fuerboeck, Christoph Perkonigg, Matthias Helbich, thomas Pinker, Katja Romeo, Valeria Langs, Georg Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy Computat Imaging Res Lab Vienna Austria Med Univ Vienna Div Mol & Struct Preclin Imaging Dept Biomed Imaging & Image Guided Therapy Vienna Austria Mem Sloan Kettering Canc Ctr Breast Imaging Serv Dept Radiol New York NY USA Univ Naples Federico II Dept Adv Biomed Sci Naples Italy

ISBN: (纸本)9783031164491;9783031164484

Molecular breast cancer sub-types derived from core-biopsy are central for individual outcome prediction and treatment decisions. Determining sub-types by non-invasive imaging procedures would benefit early assessment. Furthermore, identifying phenotypic traits of subtypes may inform our understanding of disease processes as we become able to monitor them longitudinally. We propose a model to learn phenotypic appearance concepts of four molecular sub-types of breast cancer. A deep neural network classification model predicts sub-types from multimodal, multi-parametric imaging data. Intermediate representations of the visual information are clustered, and clusters are scored based on testing with concept activation vectors to assess their contribution to correctly discriminating sub-types. the proposed model can predict subtypes with competitive accuracy from simultaneous F-18-FDG PET/MRI, and identifies visual traits in the form of shared and discriminating phenotypic concepts associated with the sub-types.

关键词： Explainable AI Breast cancer sub-types Deep learning

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Self-supervised 3D Anatomy Segmentation Using Self-distilled Masked image Transformer (SMIT) 1

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

作者： Jiang, Jue Tyagi, Neelam Tringale, Kathryn Crane, Christopher Veeraraghavan, Harini Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10021 USA Mem Sloan Kettering Canc Ctr Dept Radiat Oncol 1275 York Ave New York NY 10021 USA

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Vision transformers efficiently model long-range context and thus have demonstrated impressive accuracy gains in several image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a self-distillation learning with masked image modeling method to perform SSL for vision transformers (SMIT) applied to 3D multi-organ segmentation from CT and MRI. Our contribution combines a dense pixel-wise regression pretext task performed within masked patches called masked image prediction with masked patch token distillation to pre-train vision transformers. Our approach is more accurate and requires fewer fine tuning datasets than other pretext tasks. Unlike prior methods, which typically used image sets arising from disease sites and imaging modalities corresponding to the target tasks, we used 3,643 CT scans (602,708 images) arising from head and neck, lung, and kidney cancers as well as COVID-19 for pre-training and applied it to abdominal organs segmentation from MRI pancreatic cancer patients as well as publicly available 13 different abdominal organs segmentation from CT. Our method showed clear accuracy improvement (average DSC of 0.875 from MRI and 0.878 from CT) with reduced requirement for fine-tuning datasets over commonly used pretext tasks. Extensive comparisons against multiple current SSL methods were done. Our code is available at: https://***/harveerar/***.

关键词： Self-supervised learning Segmentation Self-distillation Masked image modeling Masked embedding transformer

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Online Reflective Learning for Robust medical image Segmentation 25th

Online Reflective Learning for Robust Medical Image Segmenta...

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

作者： Huang, Yuhao Yang, Xin Huang, Xiaoqiong Liang, Jiamin Zhou, Xinrui Chen, Cheng Dou, Haoran Hu, Xindi Cao, Yan Ni, Dong Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Leeds England Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9783031164521;9783031164514

Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-scale clinical application of deep models. In this study, inspired by human learning cycle, we propose a novel online reflective learning framework (RefSeg) to improve segmentation robustness. Based on the reflection-on-action conception, our RefSeg firstly drives the deep model to take action to obtain semantic segmentation. then, RefSeg triggers the model to reflect itself. Because making deep models realize their segmentation failures during testing is challenging, RefSeg synthesizes a realistic proxy image from the semantic mask to help deep models build intuitive and effective reflections. this proxy translates and emphasizes the segmentation flaws. By maximizing the structural similarity between the raw input and the proxy, the reflection-on-action loop is closed with segmentation robustness improved. RefSeg runs in the testing phase and is general for segmentation models. Extensive validation on three medical image segmentation tasks with a public cardiac MR dataset and two in-house large ultrasound datasets show that our RefSeg remarkably improves model robustness and reports state-of-the-art performance over strong competitors.

关键词： Segmentation Robustness Online learning

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Joint Class-Affinity Loss Correction for Robust medical image Segmentation with Noisy Labels 1

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

作者： Guo, Xiaoqing Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Kowloon Tong Hong Kong Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations. Previous segmentation arts of learning with noisy labels merely perform a pixel-wise manner to preserve semantics, such as pixel-wise label correction, but neglect the pair-wise manner. In fact, we observe that the pair-wise manner capturing affinity relations between pixels can greatly reduce the label noise rate. Motivated by this observation, we present a novel perspective for noisy mitigation by incorporating both pixel-wise and pair-wise manners, where supervisions are derived from noisy class and affinity labels, respectively. Unifying the pixel-wise and pair-wise manners, we propose a robust Joint Class-Affinity Segmentation (JCAS) framework to combat label noise issues in medical image segmentation. Considering the affinity in pair-wise manner incorporates contextual dependencies, a differentiated affinity reasoning (DAR) module is devised to rectify the pixel-wise segmentation prediction by reasoning about intra-class and inter-class affinity relations. To further enhance the noise resistance, a class-affinity loss correction (CALC) strategy is designed to correct supervision signals via the modeled noise label distributions in class and affinity labels. Meanwhile, CALC strategy interacts the pixel-wise and pair-wise manners through the theoretically derived consistency regularization. Extensive experiments under both synthetic and real-world noisy labels corroborate the efficacy of the proposed JCAS framework with a minimum gap towards the upper bound performance. the source code is available at https://***/CityU-AIM-Group/JCAS.

关键词： Class and affinity Loss correction Noisy label

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Variational Auto encoders for Improved Breast Cancer Classification 5

Variational Auto encoders for Improved Breast Cancer Classif...

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5th international conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Sreelekshmi, V. Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Among women breast cancer is the second leading cause of cancer. Emergence of Artificial Intelligence(AI) in the medical care leads to good survival rate by diagnosing and effective prognosis of the breast cancer patients. Scientific findings show that supervised deep learning model is highly dependent on the size of the training set, which must be manually labeled by experienced radiologists. the freely available biomedical imaging dataset are small in size mostly. In addition, obtaining large medical image files is difficult due to privacy and legal reasons. thus the deep learning models tend to overfit and fail to provide a general result. Also the recent studies indicate that early detection of breast cancer (BC) is crucial to achieve favorable treatment results and reduce associated mortality. Data augmentation is the most widely used approach to address the aforementioned problem. Our proposed architecture consist of the pectoral muscle removal of the dataset, then the variational auto encoders used for data augmentation and then the U-Net and its varients used for breast cancer classification. the dice similarity score obtained for the model after the pectoral muscle segmentation is 97.53% and the classification rate accuracy is 98.5% by use of the Attention Residual U-Net than the other two models. © 2024 the Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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