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Coffee Break + Poster Session I
Wei-Hung Weng · Simon Kohl · Aiham Taleb · Arijit Patra · Khashayar Namdar · Matthias Perkonigg · Shizhan Gong · Abdullah-Al-Zubaer Imran · Amir Abdi · Ilja Manakov · Johannes C. Paetzold · Ben Glocker · Dushyant Sahoo · Shreyas Fadnavis · Karsten Roth · Xueqing Liu · Yifan Zhang · Alexander Preuhs · Fabian Eitel · Anusua Trivedi · Tomer Weiss · Darko Stern · Liset Vazquez Romaguera · Johannes Hofmanninger · Aakash Kaku · Oloruntobiloba Olatunji · Anastasia Razdaibiedina · Tao Zhang
Multimodal Multitask Representation Learning for Metadata Prediction in Pathology – Weng, Cai, Lin, Tan, Chen [ORAL+Poster] A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities – Kohl, Romera-Paredes, Haier-Hein, Rezende, Eslami, Kohli, Zisserman, Ronneberger [ORAL+Poster] Task incremental learning of Chest X-ray data on compact architectures – Patra [ORAL+Poster] Multimodal Self-Supervised Learning for Medical Image Analysis – Taleb, Lippert, Nabi, Klein [ORAL+Poster] Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection – Namdar, Gujrathi, Haider, Khalvati [ORAL+Poster] Unsupervised deep clustering for predictive texture pattern discovery in medical images – Perkonigg, Sobotka, Ba-Ssalamah, Langs [ORAL+Poster] Large-scale classification of breast MRI exams using deep convolutional networks – Gong, Muckley, Wu, Makino, Kim, Heacock, Moy, Knoll, Geras [ORAL+Poster] Bipartite Distance For Shape-Aware Landmark Detection in Spinal X-Rays – Zubaer, Huang, Fan, Cheung, To, Qian, Terzopoulos GAN-enhanced Conditional Echocardiogram – Abdi, Tsang, Abolmaesumi Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks – Li, Ma, Li Push it to the Limit: Discover Edge-Cases in Image Data with Autoencoders – Manakov, Tresp, Maximilian Noise-aware PET image Enhancement with Adaptive Deep Learning – Xiang, Wang, Gong, Zaharchuk, Zhang clDice - a Novel Connectivity-Preserving Loss Function for Vessel Segmentation – Paetzold, Shit, Ezhov, Tetteh, Ertuerk, Menze Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects – Glocker, Robinson, Coelho de Castro, Dou, Konukoglu Extraction of hierarchical functional connectivity components in human brain using resting-state fMRI – Sahoo, Bassett, Davatzikos A Study into Echocardiography View Conversion – Abdi, Jafari, Fels, Tsang, Abolmaesumi Variable Projection optimization for Intravoxel Incoherent Motion (IVIM) MRI estimation – Fadnavis, Garyfallidis Boosting Liver and Lesion Segmentation from CT Scans by Mask Mining – Roth, Konopczynski, Hesser Unsupervised Sparse-view Backprojection via Convolutional and Spatial Transformer Networks – Liu, Sajda Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis – Zhang, Wei, Zhao, Niu, Wu, Tan, Huang Image Quality Assessment for Rigid Motion Compensation – Preuhs, Manhart, Roser, Stimpel, Syben, Psychogios, Kowarschik, Maier Harnessing spatial MRI normalization: patch individual filter layers for CNNs – Eitel, Albrecht, Paul, Ritter Binary Mode Multinomial Deep Learning Model for more efficient Automated Diabetic Retinopathy Detection – Trivedi, Desbiens, Gross, Ferres, Dodhia PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI – Weiss, Senouf, Vedula Variational Inference and Bayesian CNNs for Uncertainty Estimation in Multi-Factorial Bone Age Prediction – Stern, Urschler, Payer, Eggenreich In-plane organ motion prediction using a recurrent encoder-decoder framework – Vazquez Romaguera, Plantefeve, Kadoury Separation of target anatomical structure and occlusions in thoracic X-ray images – Hofmanninger, Langs Knee Cartilage Segmentation Using Diffusion-Weighted MRI – Duarte, Hedge, Kaku, Mohan, Raya Learning to estimate label uncertainty for automatic radiology report parsing – Olatunji, Yao Multi-defect microscopy image restoration under limited data conditions – Razdaibiedina, Velayutham, Modi
Author Information
Wei-Hung Weng (MIT)
Simon Kohl (DeepMind, Karlsruhe Institute of Technology)
Aiham Taleb (Hasso-Plattner-Institute, Potsdam University)
Arijit Patra (University of Oxford)
Khashayar Namdar (LTRI)
Matthias Perkonigg (Medical University of Vienna)
Shizhan Gong (New York University)
Abdullah-Al-Zubaer Imran (University of California, Los Angeles)
Amir Abdi (University of British Columbia)
Senior Machine Learning Scientist / Engineer
Ilja Manakov (LMU Munich)
Johannes C. Paetzold (Technical University of Munich (TUM))
Ben Glocker (Imperial College London)
Dushyant Sahoo (University of Pennsylvania)
Shreyas Fadnavis (Indiana University Bloomington)
Image Processing, Computer Vision, Optimization, Applied Mathematics and Neuroimaging
Karsten Roth (Heidelberg University)
Xueqing Liu (Columbia University)
Yifan Zhang (South China University of Technology)
Alexander Preuhs (Friedrich-Alexander-Universität Erlangen-Nürnberg)
Fabian Eitel (Charité - University Hospital Berlin)
Anusua Trivedi (Microsoft)
Tomer Weiss (Technion, Israel)
Darko Stern (TU Graz)
Liset Vazquez Romaguera (Polytechnique Montreal)
Johannes Hofmanninger (Medical University Vienna)
Aakash Kaku (New York University)
Oloruntobiloba Olatunji (Enlitic)
Anastasia Razdaibiedina (Vector Institute)
Anastasia is a computational biology PhD student at the University of Toronto and a postgraduate affiliate at the Vector Institute for Artificial Intelligence. Her research lies at the intersection of biomedical imaging and computer vision, and she is co-supervised by Prof. Brenda Andrews and Prof. Jimmy Ba. In her work, Anastasia focuses on GAN-based biomedical image restoration and development of deep-learning based unsupervised method that predicts how different proteins function in a cell. In addition to that, Anastasia is interested in domain adaptation and semi-supervised learning.
Tao Zhang (Subtle Medical Inc)
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2018 Workshop: Medical Imaging meets NIPS »
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