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11th international conference on computer Modeling and Simulation (ICCMS) / 8th international conference on Intelligent computing and Applications (ICICA)

作者： Miyamoto, Ryusuke Migita, Yuta Izumo, Shunya Kobayashi, Shingo Oki, Takuro Yomo, Hiroyuki Hara, Shinsuke Meiji Univ Sch Sci & Technol Dept Comp Sci Tokyo Japan Meiji Univ Grad Sch Sci & Technol Dept Comp Sci Tokyo Japan Kansai Univ Fac Engn Sci Dept Elect & Engn Suita Osaka Japan Osaka City Univ Grad Sch Engn Osaka Japan

ISBN: (纸本)9781450366199

the authors are trying to construct a real-time vital sensing system during exercise based on image-assisted routing. In order to estimate locations of target exercisers based on image processing required for multi-hop networking, robust personal identification is indispensable because visual target tracking executed after detection sometimes fails in practical scenes. To solve this problem, dynamic relocation of UAVs to obtain visual cues to identify who is a target is adopted to our system. In this paper, a CG-based simulator that has functions necessary for evaluation of dynamic relocation algorithm of UAVs. Experimental results using a basic scenario show that the implemented simulator works well.

关键词： A CG-based simulator image-assisted routing visual object localization

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High-order attention networks for medical image segmentation 23rd

High-order attention networks for medical image segmentation

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

作者： Ding, Fei Yang, Gang Wu, Jun Ding, Dayong Xv, Jie Cheng, Gangwei Li, Xirong AI & Media Computing Lab School of Information Renmin University of China Beijing China MOE Key Lab of DEKE Renmin University of China Beijing China Northwestern Polytechnical University Xian China Vistel AI Lab Visionary Intelligence Ltd. Beijing China Beijing Tongren Hospital Beijing China Peking Union Medical College Hospital Beijing China

ISBN: (纸本)9783030597092

Segmentation is a fundamental task in medical image analysis. Current state-of-the-art Convolutional Neural Networks on medical image segmentation capture local context information using fixed-shape receptive fields and feature detectors with position-invariant weights, which limits the robustness to the variance of input, such as medical objects of variant sizes, shapes, and domains. In order to capture global context information, we propose High-order Attention (HA), a novel attention module with adaptive receptive fields and dynamic weights. HA allows each pixel to has its own global attention map that models its relationship to all other pixels. In particular, HA constructs the attention map through graph transduction and thus captures high relevant context information at high-order. Consequently, feature maps at each position are selectively aggregated as a weighted sum of feature maps at all positions. We further embed the proposed HA module into an efficient encoder-decoder structure for medical image segmentation, namely High-order Attention Network (HANet). Extensive experiments are conducted on four benchmark sets for three tasks, i.e., REFUGE and Drishti-GS1 for optic disc/cup segmentation, DRIVE for blood vessel segmentation, and LUNA for lung segmentation. the results justify the effectiveness of the new attention module for medical image segmentation. © Springer Nature Switzerland AG 2020.

关键词： Pixels

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Scientific discovery by generating counterfactuals using image translation 23rd

Scientific discovery by generating counterfactuals using ima...

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

作者： Narayanaswamy, Arunachalam Venugopalan, Subhashini Webster, Dale R. Peng, Lily Corrado, Greg S. Ruamviboonsuk, Paisan Bavishi, Pinal Brenner, Michael Nelson, Philip C. Varadarajan, Avinash V. Google Research Mountain View United States Google Health Palo Alto United States Rajavithi Hospital Bangkok Thailand Google Research Cambridge United States

ISBN: (纸本)9783030597092

Model explanation techniques play a critical role in understanding the source of a models performance and making its decisions transparent. Here we investigate if explanation techniques can also be used as a mechanism for scientific discovery. We make three contributions: first, we propose a framework to convert predictions from explanation techniques to a mechanism of discovery. Second, we show how generative models in combination with black-box predictors can be used to generate hypotheses (without human priors) that can be critically examined. third, with these techniques we study classification models for retinal images predicting Diabetic Macular Edema (DME), where recent work[30] showed that a CNN trained on these images is likely learning novel features in the image. We demonstrate that the proposed framework is able to explain the underlying scientific mechanism, thus bridging the gap between the models performance and human understanding. © Springer Nature Switzerland AG 2020.

关键词： medical imaging

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Automatic data augmentation for 3d medical image segmentation 23rd

Automatic data augmentation for 3d medical image segmentatio...

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

作者： Xu, Ju Li, Mengzhang Zhu, Zhanxing Center for Data Science Peking University Beijing China Canon Medical Systems Beijing China School of Mathematical Sciences Peking University Beijing China

ISBN: (纸本)9783030597092

Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks. It could enrich diversity of training samples that is essential in medical image segmentation tasks because 1) the scale of medical image dataset is typically smaller, which may increase the risk of overfitting;2) the shape and modality of different objects such as organs or tumors are unique, thus requiring customized data augmentation policy. However, most data augmentation implementations are hand-crafted and suboptimal in medical image processing. To fully exploit the potential of data augmentation, we propose an efficient algorithm to automatically search for the optimal augmentation strategies. We formulate the coupled optimization w.r.t. network weights and augmentation parameters into a differentiable form by means of stochastic relaxation. this formulation allows us to apply alternative gradient-based methods to solve it, i.e. stochastic natural gradient method with adaptive step-size. To the best of our knowledge, it is the first time that differentiable automatic data augmentation is employed in medical image segmentation tasks. Our numerical experiments demonstrate that the proposed approach significantly outperforms existing build-in data augmentation of state-of-the-art models. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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Domain aware medical image classifier interpretation by counterfactual impact analysis 23rd

Domain aware medical image classifier interpretation by coun...

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

作者： Lenis, Dimitrios Major, David Wimmer, Maria Berg, Astrid Sluiter, Gert Bühler, Katja VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH Vienna Austria

ISBN: (纸本)9783030597092

the success of machine learning methods for computer vision tasks has driven a surge in computer assisted prediction for medicine and biology. Based on a data-driven relationship between input image and pathological classification, these predictors deliver unprecedented accuracy. Yet, the numerous approaches trying to explain the causality of this learned relationship have fallen short: time constraints, coarse, diffuse and at times misleading results, caused by the employment of heuristic techniques like Gaussian noise and blurring, have hindered their clinical adoption. In this work, we discuss and overcome these obstacles by introducing a neural-network based attribution method, applicable to any trained predictor. Our solution identifies salient regions of an input image in a single forward-pass by measuring the effect of local image-perturbations on a predictors score. We replace heuristic techniques with a strong neighborhood conditioned inpainting approach, avoiding anatomically implausible, hence adversarial artifacts. We evaluate on public mammography data and compare against existing state-of-the-art methods. Furthermore, we exemplify the approachs generalizability by demonstrating results on chest X-rays. Our solution shows, both quantitatively and qualitatively, a significant reduction of localization ambiguity and clearer conveying results, without sacrificing time efficiency. © Springer Nature Switzerland AG 2020.

关键词： Conveying

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Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images

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medical image ANALYSIS 2019年 57卷 186-196页

作者： Karimi, Davood Zeng, Qi Mathur, Prateek Avinash, Apeksha Mandavi, Sara Spadinger, Ingrid Abolmaesumi, Purang Salcudean, Septimiu E. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Canc Ctr Vancouver BC Canada

the goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. these images can be difficult to segment because of weak or insufficient landmarks or strong artifacts. We devise a method, based on convolutional neural networks (CNNs), that produces accurate segmentations on easy and difficult images alike. We propose two strategies to achieve improved segmentation accuracy on difficult images. First, for CNN training we adopt an adaptive sampling strategy, whereby the training process is encouraged to pay more attention to images that are difficult to segment. Secondly, we train a CNN ensemble and use the disagreement among this ensemble to identify uncertain segmentations and to estimate a segmentation uncertainty map. We improve uncertain segmentations by utilizing the prior shape information in the form of a statistical shape model. Our method achieves Hausdorff distance of 2.7 +/- 2.3 mm and Dice score of 93.9 +/- 3.5%. Comparisons with several competing methods show that our method achieves significantly better results and reduces the likelihood of committing large segmentation errors. Furthermore, our experiments show that our approach to estimating segmentation uncertainty is better than or on par with recent methods for estimation of prediction uncertainty in deep learning models. Our study demonstrates that estimation of model uncertainty and use of prior shape information can significantly improve the performance of CNN-based medical image segmentation methods, especially on difficult images. (C) 2019 Elsevier B.V. All rights reserved.

关键词： image segmentation Model uncertainty Shape models Clustering Deep learning

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Searching Learning Strategy with Reinforcement Learning for 3D medical image Segmentation 22nd

Searching Learning Strategy with Reinforcement Learning for ...

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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： Yang, Dong Roth, Holger Xu, Ziyue Milletari, Fausto Zhang, Ling Xu, Daguang NVIDIA Bethesda MD USA

ISBN: (纸本)9783030322458;9783030322441

Deep neural network (DNN) based approaches have been widely investigated and deployed in medical image analysis. For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation. Even the baseline neural network models (U-Net, V-Net, etc.) have been proven to be very effective and efficient when the training process is set up properly. Nevertheless, to fully exploit the potentials of neural networks, we propose an automated searching approach for the optimal training strategy with reinforcement learning. the proposed approach can be utilized for tuning hyper-parameters, and selecting necessary data augmentation with certain probabilities. the proposed approach is validated on several tasks of 3D medical image segmentation. the performance of the baseline model is boosted after searching, and it can achieve comparable accuracy to other manually-tuned state-of-theart segmentation approaches.

关键词： Reinforcement learning

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Ms-nas: Multi-scale neural architecture search for medical image segmentation 23rd

Ms-nas: Multi-scale neural architecture search for medical i...

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

作者： Yan, Xingang Jiang, Weiwen Shi, Yiyu Zhuo, Cheng ZheJiang University Hangzhou China University of Notre Dame Notre Dame United States

ISBN: (纸本)9783030597092

the recent breakthroughs of Neural Architecture Search (NAS) have motivated various applications in medical image segmentation. However, most existing work either simply rely on hyper-parameter tuning or stick to a fixed network backbone, thereby limiting the underlying search space to identify more efficient architecture. this paper presents a Multi-Scale NAS (MS-NAS) framework that is featured with multi-scale search space from network backbone to cell operation, and multi-scale fusion capability to fuse features with different sizes. To mitigate the computational overhead due to the larger search space, a partial channel connection scheme and a two-step decoding method are utilized to reduce computational overhead while maintaining optimization quality. Experimental results show that on various datasets for segmentation, MS-NAS outperforms the state-of-the-art methods and achieves 0.65.4% mIOU and 0.43.5% DSC improvements, while the computational resource consumption is reduced by 18.024.9%. © Springer Nature Switzerland AG 2020.

关键词： image segmentation

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Curriculum Semi-supervised Segmentation 22nd

Curriculum Semi-supervised Segmentation

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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： Kervadec, Hoel Dolz, Jose Granger, Eric Ben Ayed, Ismail ETS Montreal Montreal PQ Canada

ISBN: (纸本)9783030322458;9783030322441

this study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of the target region. these regressions are used to effectively regularize the segmentation network, constraining the softmax predictions of the unlabeled images to match the inferred label distributions. Our framework is based on inequality constraints, which tolerate uncertainties in the inferred knowledge, e.g., regressed region size. It can be used for a large variety of region attributes. We evaluated our approach for left ventricle segmentation in magnetic resonance images (MRI), and compared it to standard proposal-based semi-supervision strategies. Our method achieves competitive results, leveraging unlabeled data in a more efficient manner and approaching full-supervision performance.

关键词： image segmentation Semi-supervised learning Constrained CNNs

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4th international Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2016 held in conjunction on 19th international conference on medical image computing and computer assisted intervention, MICCAI 2016

4th International Workshop and Challenge on Computational Me...

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4th international Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2016 held in conjunction on 19th international conference on medical image computing and computer assisted intervention, MICCAI 2016

ISBN: (纸本)9783319550497

the proceedings contain 13 papers. the special focus in this conference is on Segmentation, Localization, computer Aided Diagnosis and intervention. the topics include: Improving an active shape model with random classification forest for segmentation of cervical vertebrae;machine learning based bone segmentation in ultrasound;variational segmentation of the white and gray matter in the spinal cord using a shape prior;automated intervertebral disc segmentation using deep convolutional neural networks;fully automatic localisation of vertebrae in CT images using random forest regression voting;global localization and orientation of the cervical spine in X-ray images;accurate intervertebral disc localisation and segmentation in MRI using vantage point Hough forests and multi-atlas fusion;multi-scale and modality dropout learning for intervertebral disc localization and segmentation;fully automatic localization and segmentation of intervertebral disc from 3D multi-modality MR images by regression forest and CNN;manual and computer-assisted pedicle screw placement plans: a quantitative comparison;detection of degenerative osteophytes of the spine on PET/CT using region-based convolutional neural networks;reconstruction of 3D Lumvar vertebra from two X-ray images based on 2D/3D registration and classification of progressive and non-progressive scoliosis patients using discriminant manifolds.

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