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

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Supervised Uncertainty Quantification for Segmentation with Multiple Annotations 22nd

Supervised Uncertainty Quantification for Segmentation with ...

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Hu, Shi Worrall, Daniel Knegt, Stefan Veeling, Bas Huisman, Henkjan Welling, Max Univ Amsterdam Amsterdam Netherlands Radboud Univ Nijmegen Med Ctr Nijmegen Netherlands

ISBN: (纸本)9783030322458;9783030322441

the accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variability as a source of 'groundtruth' aleatoric uncertainty, which can be treated as a target in a supervised learning problem. We combine this groundtruth uncertainty with a Probabilistic U-Net and test on the LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find that we are able to improve predictive uncertainty estimates. We also find that we can improve sample accuracy and sample diversity. In real-world applications, our method could inform doctors about the confidence of the segmentation results.

关键词： Uncertainty image segmentation Deep learning

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Evidence Localization for Pathology images Using Weakly Supervised Learning 22nd

Evidence Localization for Pathology Images Using Weakly Supe...

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Huang, Yongxiang Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Lo Kwee Seong Med Image Anal Lab Hong Kong Peoples R China

ISBN: (纸本)9783030322397;9783030322380

Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization. To overcome this problem, we propose a weakly supervised learning-based approach that can effectively learn to localize the discriminative evidence for a diagnostic label from weakly labeled training data. Experimental results show that our proposed method can reliably pinpoint the location of cancerous evidence supporting the decision of interest, while still achieving a competitive performance on glimpse-level and slide-level histopathologic cancer detection tasks.

关键词： Pathology image detection Weakly-supervised learning computer-aided diagnosis

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7th international Workshop on Machine Learning in medical Imaging, MLMI 2016 held in conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

7th International Workshop on Machine Learning in Medical Im...

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7th international Workshop on Machine Learning in medical Imaging, MLMI 2016 held in conjunction with 19th international conference on medical image computing and computer-assisted intervention, MICCAI 2016

ISBN: (纸本)9783319471563

the proceedings contain 38 papers. the special focus in this conference is on Machine Learning in medical Imaging. the topics include: Identifying high order brain connectome biomarkers via learning on hypergraph;bilateral regularization in reproducing kernel Hilbert spaces for discontinuity preserving image registration;building an ensemble of complementary segmentation methods by exploiting probabilistic estimates;learning appearance and shape evolution for infant image registration in the first year of life;detecting osteophytes in radiographs of the knee to diagnose osteoarthritis;direct estimation of fiber orientations using deep learning in diffusion imaging;dual-layer groupwise registration for consistent labeling of longitudinal brain images;joint discriminative and representative feature selection for Alzheimer’s disease diagnosis;patch-based hippocampus segmentation using a local subspace learning method;a semi-supervised large margin algorithm for white matter hyperintensity segmentation;deep ensemble sparse regression network for Alzheimer’s disease diagnosis;learning representation for histopathological image with quaternion grassmann average network;learning global and cluster-specific classifiers for robust brain extraction in MR data;segmentation-free estimation of kidney volumes in CT with dual regression forests;retinal image quality classification using saliency maps and CNNs;unsupervised discovery of emphysema subtypes in a large clinical cohort;tree-based transforms for privileged learning;automated 3d ultrasound biometry planes extraction for first trimester fetal assessment and learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.

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Unsupervised Quality Control of image Segmentation Based on Bayesian Learning 22nd

Unsupervised Quality Control of Image Segmentation Based on ...

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Audelan, Benoit Delingette, Herve Univ Cote Azur INRIA Epione Project Team Sophia Antipolis France

ISBN: (纸本)9783030322458;9783030322441

Assessing the quality of segmentations on an image database is required as many downstream clinical applications are based on segmentation results. For large databases, this quality assessment becomes tedious for a human expert and therefore some automation of this task is necessary. In this paper, we introduce a novel unsupervised approach to assist the quality control of image segmentations by measuring their adequacy with segmentations produced by a generic probabilistic model. To this end, we introduce a new segmentation model combining intensity and a spatial prior defined through a combination of spatially smooth kernels. the tractability of the approach is obtained by solving a type-II maximum likelihood which directly estimates hyperparameters. Assessing the quality of the segmentation with respect to the probabilistic model allows to detect the most challenging cases inside a dataset. this approach was evaluated on the BRATS 2017 and ACDC datasets showing its relevance for quality control assessment.

关键词： Quality control image segmentation Bayesian learning

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Diverse Multiple Prediction on Neuron image Reconstruction 22nd

Diverse Multiple Prediction on Neuron Image Reconstruction

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Ye, Ze Chen, Cong Yuan, Changhe Chen, Chao SUNY Stony Brook Stony Brook NY 11794 USA CUNY New York NY 10021 USA

ISBN: (纸本)9783030322397;9783030322380

Neuron reconstruction from anisotropic 3D Electron Microscopy (EM) images is a challenging problem. One often considers an input image as a stack of 2D image slices, and consider both intra and inter slice segments information. In this paper, we present a new segmentation algorithm which builds a unified energy function and jointly optimize the per-slice segmentation and the inter-slice consistency. To find an optimal solution from the huge solution space, we propose a novel diverse multiple prediction method which also encourages diversity in partial solutions. We demonstrate the strength of our method in several public datasets.

关键词： image reconstruction

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Comparing to learn: Surpassing imagenet pretraining on radiographs by comparing image representations 23rd

Comparing to learn: Surpassing imagenet pretraining on radio...

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23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

作者： Zhou, Hong-Yu Yu, Shuang Bian, Cheng Hu, Yifan Ma, Kai Zheng, Yefeng Jarvis Lab Tencent Shenzhen China

ISBN: (纸本)9783030597092

In deep learning era, pretrained models play an important role in medical image analysis, in which imageNet pretraining has been widely adopted as the best way. However, it is undeniable that there exists an obvious domain gap between natural images and medical images. To bridge this gap, we propose a new pretraining method which learns from 700k radiographs given no manual annotations. We call our method as Comparing to Learn (C2L) because it learns robust features by comparing different image representations. To verify the effectiveness of C2L, we conduct comprehensive ablation studies and evaluate it on different tasks and datasets. the experimental results on radiographs show that C2L can outperform imageNet pretraining and previous state-of-the-art approaches significantly. Code and models are available at https://***/funnyzhou/C2L_MICCAI2020. © Springer Nature Switzerland AG 2020.

关键词： medical imaging

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Learning and exploiting interclass visual correlations for medical image classification 23rd

Learning and exploiting interclass visual correlations for m...

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23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

作者： Wei, Dong Cao, Shilei Ma, Kai Zheng, Yefeng Tencent Jarvis Lab Shenzhen China

ISBN: (纸本)9783030597092

Deep neural network-based medical image classifications often use "hard" labels for training, where the probability of the correct category is 1 and those of others are 0. However, these hard targets can drive the networks over-confident about their predictions and prone to overfit the training data, affecting model generalization and adaption. Studies have shown that label smoothing and softening can improve classification performance. Nevertheless, existing approaches are either non-data-driven or limited in applicability. In this paper, we present the Class-Correlation Learning Network (CCL-Net) to learn interclass visual correlations from given training data, and produce soft labels to help with classification tasks. Instead of letting the network directly learn the desired correlations, we propose to learn them implicitly via distance metric learning of class-specific embeddings with a lightweight plugin CCL block. An intuitive loss based on a geometrical explanation of correlation is designed for bolstering learning of the interclass correlations. We further present end-to-end training of the proposed CCL block as a plugin head together with the classification backbone while generating soft labels on the fly. Our experimental results on the international Skin Imaging Collaboration 2018 dataset demonstrate effective learning of the interclass correlations from training data, as well as consistent improvements in performance upon several widely used modern network structures with the CCL block. © Springer Nature Switzerland AG 2020.

关键词： computer aided diagnosis

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image-and-Spatial Transformer Networks for Structure-Guided image Registration 22nd

Image-and-Spatial Transformer Networks for Structure-Guided ...

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Lee, Matthew C. H. Oktay, Ozan Schuh, Andreas Schaap, Michiel Glocker, Ben HeartFlow Redwood City CA 94063 USA Imperial Coll London Biomed Image Anal Grp London England

ISBN: (纸本)9783030322458;9783030322441

image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. the goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures. We argue and show that the current direct, non-iterative approaches are sub-optimal, in particular if we seek accurate alignment of Structures-of-Interest (SoI). Information about SoI is often available at training time, for example, in form of segmentations or landmarks. We introduce a novel, generic framework, image-and-Spatial Transformer Networks (ISTNs), to leverage SoI information allowing us to learn new image representations that are optimised for the downstream registration task. thanks to these representations we can employ a test-specific, iterative refinement over the transformation parameters which yields highly accurate registration even with very limited training data. Performance is demonstrated on pairwise 3D brain registration and illustrative synthetic data.

关键词： image registration

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PAN: Projective Adversarial Network for medical image Segmentation 22nd

PAN: Projective Adversarial Network for Medical Image Segmen...

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Khosravan, Naji Mortazi, Aliasghar Wallace, Michael Bagci, Ulas Mayo Clin Canc Ctr Jacksonville FL 32224 USA Univ Cent Florida Sch Comp Sci Ctr Res Comp Vis CRCV Orlando FL 32816 USA

ISBN: (纸本)9783030322267;9783030322250

Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.

关键词： Object segmentation Deep learning Adversarial learning Attention Projective Pancreas

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Assessing Reliability and Challenges of Uncertainty Estimations for medical image Segmentation 22nd

Assessing Reliability and Challenges of Uncertainty Estimati...

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10th international Workshop on Machine Learning in medical Imaging (MLMI) / 22nd international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Jungo, Alain Reyes, Mauricio Univ Bern Insel Data Sci Ctr Inselspital Univ Hosp Bern Bern Switzerland

ISBN: (纸本)9783030322458;9783030322441

Despite the recent improvements in overall accuracy, deep learning systems still exhibit low levels of robustness. Detecting possible failures is critical for a successful clinical integration of these systems, where each data point corresponds to an individual patient. Uncertainty measures are a promising direction to improve failure detection since they provide a measure of a system's confidence. Although many uncertainty estimation methods have been proposed for deep learning, little is known on their benefits and current challenges for medical image segmentation. therefore, we report results of evaluating common voxel-wise uncertainty measures with respect to their reliability, and limitations on two medical image segmentation datasets. Results show that current uncertainty methods perform similarly and although they are well-calibrated at the dataset level, they tend to be miscalibrated at subject-level. therefore, the reliability of uncertainty estimates is compromised, highlighting the importance of developing subject-wise uncertainty estimations. Additionally, among the benchmarked methods, we found auxiliary networks to be a valid alternative to common uncertainty methods since they can be applied to any previously trained segmentation model.

关键词： Uncertainty Segmentation Deep learning

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