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دانلود کتاب Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

دانلود کتاب محاسبات تصویر پزشکی و مداخله به کمک رایانه - MICCAI 2019: بیست و دومین کنفرانس بین المللی ، شنژن ، چین ، 13 الی 17 اکتبر 2019 ، مجموعه مقالات ، قسمت ششم

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

مشخصات کتاب

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

ویرایش: 1st ed. 2019 
نویسندگان: , , , , , , ,   
سری: Lecture Notes in Computer Science 11769 
ISBN (شابک) : 9783030322250, 9783030322267 
ناشر: Springer International Publishing 
سال نشر: 2019 
تعداد صفحات: 895 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 136 مگابایت 

قیمت کتاب (تومان) : 34,000

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کلمات کلیدی مربوط به کتاب محاسبات تصویر پزشکی و مداخله به کمک رایانه - MICCAI 2019: بیست و دومین کنفرانس بین المللی ، شنژن ، چین ، 13 الی 17 اکتبر 2019 ، مجموعه مقالات ، قسمت ششم: علوم کامپیوتر، پردازش تصویر و بینایی کامپیوتر، تشخیص الگو، انفورماتیک سلامت



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توجه داشته باشید کتاب محاسبات تصویر پزشکی و مداخله به کمک رایانه - MICCAI 2019: بیست و دومین کنفرانس بین المللی ، شنژن ، چین ، 13 الی 17 اکتبر 2019 ، مجموعه مقالات ، قسمت ششم نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.


توضیحاتی در مورد کتاب محاسبات تصویر پزشکی و مداخله به کمک رایانه - MICCAI 2019: بیست و دومین کنفرانس بین المللی ، شنژن ، چین ، 13 الی 17 اکتبر 2019 ، مجموعه مقالات ، قسمت ششم



مجموعه شش جلدی LNCS 11764، 11765، 11766، 11767، 11768، و 11769، مجموعه مقالات داوری بیست و دومین کنفرانس بین المللی محاسبات تصویر پزشکی و مداخله به کمک کامپیوتر، Shenzhen, MICC, MICC, 2019AI است. چین، در اکتبر 2019.

539 مقاله کامل اصلاح شده ارائه شده با دقت بررسی و از بین 1730 مورد ارسالی در یک فرآیند بررسی دوسوکور انتخاب شدند. مقالات در بخش های موضوعی زیر سازماندهی شده اند:

بخش اول: تصویربرداری نوری. آندوسکوپی؛ میکروسکوپ.

بخش دوم: تقسیم بندی تصویر. ثبت تصویر; تصویربرداری قلب و عروق؛ رشد، تکامل، آتروفی و ​​پیشرفت.

بخش سوم: بازسازی و سنتز تصویر عصبی. بخش بندی تصویر عصبی؛ تصویربرداری رزونانس مغناطیسی با وزن انتشار؛ تصویربرداری عصبی عملکردی (fMRI)؛ تصویربرداری عصبی متفرقه.

بخش چهارم: shape; پیش بینی؛ تشخیص و محلی سازی؛ فراگیری ماشین؛ تشخیص به کمک کامپیوتر؛ بازسازی و سنتز تصویر.

بخش پنجم: مداخلات به کمک کامپیوتر. MIC با CAI ملاقات می کند.

قسمت ششم: توموگرافی کامپیوتری. تصویربرداری اشعه ایکس.


توضیحاتی درمورد کتاب به خارجی

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019.

The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: optical imaging; endoscopy; microscopy.

Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression.

Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging.

Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis.

Part V: computer assisted interventions; MIC meets CAI.

Part VI: computed tomography; X-ray imaging.



فهرست مطالب

Front Matter ....Pages i-xxxviii
Front Matter ....Pages 1-1
Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma (Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille)....Pages 3-12
MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection (Zihao Li, Shu Zhang, Junge Zhang, Kaiqi Huang, Yizhou Wang, Yizhou Yu)....Pages 13-21
Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classification (Yu Zhao, Yuan Liu, Yansheng Kan, Anjany Sekuboyina, Diana Waldmannstetter, Hongwei Li et al.)....Pages 22-30
BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization (Il Yong Chun, Xuehang Zheng, Yong Long, Jeffrey A. Fessler)....Pages 31-40
Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning (Siyuan Pan, Xuhong Hou, Huating Li, Bin Sheng, Ruogu Fang, Yuxin Xue et al.)....Pages 41-49
Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacement Networks (Mattias P. Heinrich)....Pages 50-58
Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning (Dan Nguyen, Azar Sadeghnejad Barkousaraie, Chenyang Shen, Xun Jia, Steve Jiang)....Pages 59-67
PAN: Projective Adversarial Network for Medical Image Segmentation (Naji Khosravan, Aliasghar Mortazi, Michael Wallace, Ulas Bagci)....Pages 68-76
Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction (Haofu Liao, Wei-An Lin, Zhimin Huo, Levon Vogelsang, William J. Sehnert, S. Kevin Zhou et al.)....Pages 77-85
Multi-class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentation (Qin Liu, Xiongfeng Tang, Deming Guo, Yanguo Qin, Pengfei Jia, Yiqiang Zhan et al.)....Pages 86-94
LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localization (Jintai Chen, Yanjie Wang, Ruoqian Guo, Bohan Yu, Tingting Chen, Wenzhe Wang et al.)....Pages 95-103
Contextual Deep Regression Network for Volume Estimation in Orbital CT (Shikha Chaganti, Cam Bermudez, Louise A. Mawn, Thomas Lasko, Bennett A. Landman)....Pages 104-111
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images (Hristina Uzunova, Jan Ehrhardt, Fabian Jacob, Alex Frydrychowicz, Heinz Handels)....Pages 112-120
Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging (Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara et al.)....Pages 121-129
ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans (Mohammad Arafat Hussain, Ghassan Hamarneh, Rafeef Garbi)....Pages 130-138
DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy (Yuting He, Guanyu Yang, Yang Chen, Youyong Kong, Jiasong Wu, Lijun Tang et al.)....Pages 139-147
Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior (Han Zheng, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Qingqing Chen, Yutaro Iwamoto et al.)....Pages 148-156
Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network (Renzhen Wang, Shilei Cao, Kai Ma, Deyu Meng, Yefeng Zheng)....Pages 157-165
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN (Boah Kim, Jieun Kim, June-Goo Lee, Dong Hwan Kim, Seong Ho Park, Jong Chul Ye)....Pages 166-174
Volumetric Attention for 3D Medical Image Segmentation and Detection (Xudong Wang, Shizhong Han, Yunqiang Chen, Dashan Gao, Nuno Vasconcelos)....Pages 175-184
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention (Qingyi Tao, Zongyuan Ge, Jianfei Cai, Jianxiong Yin, Simon See)....Pages 185-193
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation (Ke Yan, Youbao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu et al.)....Pages 194-202
Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction (Haofu Liao, Wei-An Lin, Jianbo Yuan, S. Kevin Zhou, Jiebo Luo)....Pages 203-211
AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks (Yulei Qin, Mingjian Chen, Hao Zheng, Yun Gu, Mali Shen, Jie Yang et al.)....Pages 212-220
Integrating Cross-modality Hallucinated MRI with CT to Aid Mediastinal Lung Tumor Segmentation (Jiang Jue, Hu Jason, Tyagi Neelam, Rimner Andreas, Berry L. Sean, Deasy O. Joseph et al.)....Pages 221-229
Bronchus Segmentation and Classification by Neural Networks and Linear Programming (Tianyi Zhao, Zhaozheng Yin, Jiao Wang, Dashan Gao, Yunqiang Chen, Yunxiang Mao)....Pages 230-239
Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models (Takayasu Moriya, Hirohisa Oda, Midori Mitarai, Shota Nakamura, Holger R. Roth, Masahiro Oda et al.)....Pages 240-248
Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation (Mehdi Astaraki, Iuliana Toma-Dasu, Örjan Smedby, Chunliang Wang)....Pages 249-256
mlVIRNET: Multilevel Variational Image Registration Network (Alessa Hering, Bram van Ginneken, Stefan Heldmann)....Pages 257-265
NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation (Hao Tang, Chupeng Zhang, Xiaohui Xie)....Pages 266-274
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition (Zeju Li, Han Li, Hu Han, Gonglei Shi, Jiannan Wang, S. Kevin Zhou)....Pages 275-283
Targeting Precision with Data Augmented Samples in Deep Learning (Pietro Nardelli, Raúl San José Estépar)....Pages 284-292
Pulmonary Vessel Segmentation Based on Orthogonal Fused U-Net++ of Chest CT Images (Hejie Cui, Xinglong Liu, Ning Huang)....Pages 293-300
Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-scale Booster (Qingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng)....Pages 301-309
Deep Variational Networks with Exponential Weighting for Learning Computed Tomography (Valery Vishnevskiy, Richard Rau, Orcun Goksel)....Pages 310-318
R\(^{2}\)-Net: Recurrent and Recursive Network for Sparse-View CT Artifacts Removal (Tiancheng Shen, Xia Li, Zhisheng Zhong, Jianlong Wu, Zhouchen Lin)....Pages 319-327
Stereo-Correlation and Noise-Distribution Aware ResVoxGAN for Dense Slices Reconstruction and Noise Reduction in Thick Low-Dose CT (Rongjun Ge, Guanyu Yang, Chenchu Xu, Yang Chen, Limin Luo, Shuo Li)....Pages 328-338
Harnessing 2D Networks and 3D Features for Automated Pancreas Segmentation from Volumetric CT Images (Huai Chen, Xiuying Wang, Yijie Huang, Xiyi Wu, Yizhou Yu, Lisheng Wang)....Pages 339-347
Tubular Structure Segmentation Using Spatial Fully Connected Network with Radial Distance Loss for 3D Medical Images (Chenglong Wang, Yuichiro Hayashi, Masahiro Oda, Hayato Itoh, Takayuki Kitasaka, Alejandro F. Frangi et al.)....Pages 348-356
Bronchial Cartilage Assessment with Model-Based GAN Regressor (Pietro Nardelli, George R. Washko, Raúl San José Estépar)....Pages 357-365
Adversarial Optimization for Joint Registration and Segmentation in Prostate CT Radiotherapy (Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti, Ivana Išgum, Marius Staring)....Pages 366-374
Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis (Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan S. Kirschke, Bjoern H. Menze)....Pages 375-383
Automatically Localizing a Large Set of Spatially Correlated Key Points: A Case Study in Spine Imaging (Alexander Oliver Mader, Cristian Lorenz, Jens von Berg, Carsten Meyer)....Pages 384-392
Permutohedral Attention Module for Efficient Non-local Neural Networks (Samuel Joutard, Reuben Dorent, Amanda Isaac, Sebastien Ourselin, Tom Vercauteren, Marc Modat)....Pages 393-401
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels (Martin Zlocha, Qi Dou, Ben Glocker)....Pages 402-410
Front Matter ....Pages 411-411
PRSNet: Part Relation and Selection Network for Bone Age Assessment (Yuanfeng Ji, Hao Chen, Dan Lin, Xiaohua Wu, Di Lin)....Pages 413-421
Mask Embedding for Realistic High-Resolution Medical Image Synthesis (Yinhao Ren, Zhe Zhu, Yingzhou Li, Dehan Kong, Rui Hou, Lars J. Grimm et al.)....Pages 422-430
TUNA-Net: Task-Oriented UNsupervised Adversarial Network for Disease Recognition in Cross-domain Chest X-rays (Yuxing Tang, Youbao Tang, Veit Sandfort, Jing Xiao, Ronald M. Summers)....Pages 431-440
Misshapen Pelvis Landmark Detection by Spatial Local Correlation Mining for Diagnosing Developmental Dysplasia of the Hip (Chuanbin Liu, Hongtao Xie, Sicheng Zhang, Jingyuan Xu, Jun Sun, Yongdong Zhang)....Pages 441-449
Adversarial Policy Gradient for Deep Learning Image Augmentation (Kaiyang Cheng, Claudia Iriondo, Francesco Calivá, Justin Krogue, Sharmila Majumdar, Valentina Pedoia)....Pages 450-458
Weakly Supervised Universal Fracture Detection in Pelvic X-Rays (Yirui Wang, Le Lu, Chi-Tung Cheng, Dakai Jin, Adam P. Harrison, Jing Xiao et al.)....Pages 459-467
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography (Li Xiao, Cheng Zhu, Junjun Liu, Chunlong Luo, Peifang Liu, Yi Zhao)....Pages 468-476
From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network (Yuhang Liu, Zhen Zhou, Shu Zhang, Ling Luo, Qianyi Zhang, Fandong Zhang et al.)....Pages 477-485
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis (Heyi Li, Dongdong Chen, William H. Nailon, Mike E. Davies, David I. Laurenson)....Pages 486-494
Uncertainty Measurements for the Reliable Classification of Mammograms (Mickael Tardy, Bruno Scheffer, Diana Mateus)....Pages 495-503
GraphX\(^\mathbf{\small NET } -\) Chest X-Ray Classification Under Extreme Minimal Supervision (Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Philip Sellars, Qingnan Fan, Robby T. Tan et al.)....Pages 504-512
3DFPN-HS\(^2\): 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection (Jingya Liu, Liangliang Cao, Oguz Akin, Yingli Tian)....Pages 513-521
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays (Vaishnavi Subramanian, Hongzhi Wang, Joy T. Wu, Ken C. L. Wong, Arjun Sharma, Tanveer Syeda-Mahmood)....Pages 522-530
Hand Pose Estimation for Pediatric Bone Age Assessment (María Escobar, Cristina González, Felipe Torres, Laura Daza, Gustavo Triana, Pablo Arbeláez)....Pages 531-539
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms (Zhusi Zhong, Jie Li, Zhenxi Zhang, Zhicheng Jiao, Xinbo Gao)....Pages 540-548
Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations (Sai Gokul Hariharan, Christian Kaethner, Norbert Strobel, Markus Kowarschik, Shadi Albarqouni, Rebecca Fahrig et al.)....Pages 549-557
LVC-Net: Medical Image Segmentation with Noisy Label Based on Local Visual Cues (Yucheng Shu, Xiao Wu, Weisheng Li)....Pages 558-566
Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation (Xiaoqian Jia, Sicheng Wang, Xiao Liang, Anjali Balagopal, Dan Nguyen, Ming Yang et al.)....Pages 567-575
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation (Haidong Zhu, Jialin Shi, Ji Wu)....Pages 576-584
Anatomical Priors for Image Segmentation via Post-processing with Denoising Autoencoders (Agostina J. Larrazabal, Cesar Martinez, Enzo Ferrante)....Pages 585-593
Simultaneous Lung Field Detection and Segmentation for Pediatric Chest Radiographs (Wei Zhang, Guanbin Li, Fuyu Wang, Longjiang E, Yizhou Yu, Liang Lin et al.)....Pages 594-602
Deep Esophageal Clinical Target Volume Delineation Using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk (Dakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam P. Harrison, Jing Xiao, Chen-kan Tseng et al.)....Pages 603-612
Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray (Xi Ouyang, Zhong Xue, Yiqiang Zhan, Xiang Sean Zhou, Qingfeng Wang, Ying Zhou et al.)....Pages 613-621
Multi-task Localization and Segmentation for X-Ray Guided Planning in Knee Surgery (Florian Kordon, Peter Fischer, Maxim Privalov, Benedict Swartman, Marc Schnetzke, Jochen Franke et al.)....Pages 622-630
Towards Fully Automatic X-Ray to CT Registration (Javier Esteban, Matthias Grimm, Mathias Unberath, Guillaume Zahnd, Nassir Navab)....Pages 631-639
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks (Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab et al.)....Pages 640-648
How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning (Maximilian Blendowski, Hannes Nickisch, Mattias P. Heinrich)....Pages 649-657
Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis (Jiancheng Yang, Rongyao Fang, Bingbing Ni, Yamin Li, Yi Xu, Linguo Li)....Pages 658-666
Extract Bone Parts Without Human Prior: End-to-end Convolutional Neural Network for Pediatric Bone Age Assessment (Chuanbin Liu, Hongtao Xie, Yizhi Liu, Zhengjun Zha, Fanchao Lin, Yongdong Zhang)....Pages 667-675
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment (Florin C. Ghesu, Bogdan Georgescu, Eli Gibson, Sebastian Guendel, Mannudeep K. Kalra, Ramandeep Singh et al.)....Pages 676-684
Adversarial Regression Training for Visualizing the Progression of Chronic Obstructive Pulmonary Disease with Chest X-Rays (Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet, Tolga Tasdizen)....Pages 685-693
Medical-based Deep Curriculum Learning for Improved Fracture Classification (Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab et al.)....Pages 694-702
Realistic Breast Mass Generation Through BIRADS Category (Hakmin Lee, Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro)....Pages 703-711
Learning from Longitudinal Mammography Studies (Shaked Perek, Lior Ness, Mika Amit, Ella Barkan, Guy Amit)....Pages 712-720
Automatic Radiology Report Generation Based on Multi-view Image Fusion and Medical Concept Enrichment (Jianbo Yuan, Haofu Liao, Rui Luo, Jiebo Luo)....Pages 721-729
Multi-label Thoracic Disease Image Classification with Cross-Attention Networks (Congbo Ma, Hu Wang, Steven C. H. Hoi)....Pages 730-738
InfoMask: Masked Variational Latent Representation to Localize Chest Disease (Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh et al.)....Pages 739-747
Longitudinal Change Detection on Chest X-rays Using Geometric Correlation Maps (Dong Yul Oh, Jihang Kim, Kyong Joon Lee)....Pages 748-756
Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation (Yunyan Xing, Zongyuan Ge, Rui Zeng, Dwarikanath Mahapatra, Jarrel Seah, Meng Law et al.)....Pages 757-765
Semi-supervised Learning by Disentangling and Self-ensembling over Stochastic Latent Space (Prashnna Kumar Gyawali, Zhiyuan Li, Sandesh Ghimire, Linwei Wang)....Pages 766-774
An Automated Cobb Angle Estimation Method Using Convolutional Neural Network with Area Limitation (Kailai Zhang, Nanfang Xu, Guosheng Yang, Ji Wu, Xiangling Fu)....Pages 775-783
Endotracheal Tube Detection and Segmentation in Chest Radiographs Using Synthetic Data (Maayan Frid-Adar, Rula Amer, Hayit Greenspan)....Pages 784-792
Learning Interpretable Features via Adversarially Robust Optimization (Ashkan Khakzar, Shadi Albarqouni, Nassir Navab)....Pages 793-800
Synthesize Mammogram from Digital Breast Tomosynthesis with Gradient Guided cGANs (Gongfa Jiang, Yao Lu, Jun Wei, Yuesheng Xu)....Pages 801-809
Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations (Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne)....Pages 810-818
Improved Inference via Deep Input Transfer (Saeid Asgari Taghanaki, Kumar Abhishek, Ghassan Hamarneh)....Pages 819-827
Neural Architecture Search for Adversarial Medical Image Segmentation (Nanqing Dong, Min Xu, Xiaodan Liang, Yiliang Jiang, Wei Dai, Eric Xing)....Pages 828-836
MeshSNet: Deep Multi-scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces (Chunfeng Lian, Li Wang, Tai-Hsien Wu, Mingxia Liu, Francisca Durán, Ching-Chang Ko et al.)....Pages 837-845
Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks (Fei-Fei Xue, Jin Peng, Ruixuan Wang, Qiong Zhang, Wei-Shi Zheng)....Pages 846-854
Back Matter ....Pages 855-860




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