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Workshop
Medical Imaging meets NeurIPS
Hervé Lombaert · Ben Glocker · Ender Konukoglu · Marleen de Bruijne · Aasa Feragen · Ipek Oguz · Jonas Teuwen

Sat Dec 14 08:00 AM -- 06:45 PM (PST) @ West 301 - 305
Event URL: https://sites.google.com/view/med-neurips-2019 »

Medical imaging and radiology are facing a major crisis with an ever-increasing complexity and volume of data along an immense economic pressure. The current advances and widespread use of imaging technologies now overload the human capacity of interpreting medical images, dangerously posing a risk of missing critical patterns of diseases. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition, which is mainly due to the domain complexity and constraints in clinical applications, i.e., robustness, high accuracy and reliability.

“Medical Imaging meets NeurIPS” aims to bring researchers together from the medical imaging and machine learning communities to discuss the major challenges in the field and opportunities for research and novel applications. The proposed event will be the continuation of a successful workshop organized in NeurIPS 2017 and 2018 (https://sites.google.com/view/med-nips-2018). It will feature a series of invited speakers from academia, medical sciences and industry to give an overview of recent technological advances and remaining major challenges.

Sat 8:00 a.m. - 8:15 a.m. [iCal]
Opening Remarks (Talks)
Hervé Lombaert, Ben Glocker, Ender Konukoglu, Marleen de Bruijne, Aasa Feragen, Ipek Oguz, Jonas Teuwen
Sat 8:15 a.m. - 9:00 a.m. [iCal]

Machine Learning in Hematology: Reinventing the Blood Test

René Vidal
Sat 9:00 a.m. - 10:00 a.m. [iCal]

09:00 – Multimodal Multitask Representation Learning for Metadata Prediction in Pathology – Weng, Cai, Lin, Tan, Chen 09:20 – A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities – Kohl, Romera-Paredes, Haier-Hein, Rezende, Eslami, Kohli, Zisserman, Ronneberger 09:40 – Task incremental learning of Chest X-ray data on compact architectures – Patra

Wei-Hung Weng, Simon Kohl, Arijit Patra
Sat 10:00 a.m. - 10:30 a.m. [iCal]

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

Wei-Hung Weng, Simon Kohl, Aiham Taleb, Arijit Patra, Ernest Namdar, Matthias Perkonigg, Peter 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, Tobi Olatunji, Anastasia Razdaibiedina, Tao Zhang
Sat 10:30 a.m. - 11:15 a.m. [iCal]

Deep learning for medical image quality control

Julia Schnabel
Sat 11:15 a.m. - 12:35 p.m. [iCal]

11:15 – Multimodal Self-Supervised Learning for Medical Image Analysis – Taleb, Lippert, Nabi, Klein 11:35 – Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection – Namdar, Gujrathi, Haider, Khalvati 11:55 – Unsupervised deep clustering for predictive texture pattern discovery in medical images – Perkonigg, Sobotka, Ba-Ssalamah, Langs 12:15 – Large-scale classification of breast MRI exams using deep convolutional networks – Gong, Muckley, Wu, Makino, Kim, Heacock, Moy, Knoll, Geras

Aiham Taleb, Ernest Namdar, Matthias Perkonigg, Peter Gong
Sat 12:35 p.m. - 2:00 p.m. [iCal]
Lunch (Break)
Sat 2:00 p.m. - 2:45 p.m. [iCal]

Changing the Paradigm of Pathology: AI and Computational Diagnostics

Leo Grady
Sat 2:45 p.m. - 3:45 p.m. [iCal]

14:45 – High Resolution Medical Image Analysis with Spatial Partitioning – Hou, Cheng, Shazeer, Parmar, Li, Korfiatis, Drucker, Blezek, Song 15:05 – Estimating localized complexity of white-matter wiring with GANs – Hallgrimsson, Sharan, Grafton, Singh 15:25 – Training a Variational Network for use on 3D High Resolution MRI Data in 1 Day – Kames, Doucette, Rauscher

Niki Parmar, Haraldur Hallgrimsson, Christian Kames
Sat 3:45 p.m. - 4:15 p.m. [iCal]

High Resolution Medical Image Analysis with Spatial Partitioning – Hou, Cheng, Shazeer, Parmar, Li, Korfiatis, Drucker, Blezek, Song [ORAL+Poster] Estimating localized complexity of white-matter wiring with GANs – Hallgrimsson, Sharan, Grafton, Singh [ORAL+Poster] Training a Variational Network for use on 3D High Resolution MRI Data in 1 Day – Kames, Doucette, Rauscher [ORAL+Poster] Saliency cues for continual learning of ultrasound – Patra End-to-End Fully Automatic Segmentation of Scoliotic Vertebrae from Spinal X-Ray Images – Imran, Huang, Tang, Fan, Cheung, To, Qian, Terzepoulos Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making – Yang, Dvornek, Zhang, Chapiro, Lin, Abajian, Duncan High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection – Zimmerer, Petersen, Maier-Hein Radiologist Validated Systematic Search over Deep Neural Networks for Screening Musculoskeletal Radiographs – Chakravarty, Sheet, Ghosh, Sarkar, Sethuraman A Biased Sampling Network to Localise Landmarks for Automated Disease Diagnosis – Schobs, Zhou, Cogliano, Swift, Lu Variational inference based assessment of mammographic lesion classification algorithms under distribution shift – Gossmann, Cha, Sun Batch-wise Dice Loss: Rethinking the Data Imbalance for Medical Image Segmentation – Chang, Lin, Wu, Chen, Hsu Towards Artifact Rejection in Microscopic Urinalysis – Dutt Analysis of focal loss with noisy labels – Yao, Jadhav Data Augmentation for Early Stage Lung Nodules using Deep Image Prior and CycleGan – Martinez Manzanera, Ellis, Baltatzis, Devaraj, Desai, Le Golgoc, Nair, Glocker, Schnabel Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands – Pinckaers, Litjens Class-Aware CycleGAN: A domain adaptation method for mammography and tomosynthesis – Dalmis, Birhanu, Vanegas, Kallenerg, Kroes Tracking-Assisted Segmentation of Biological Cells – Gupta, de Bruin, Panteli, Gavves Deep learning feature based medical image retrieval for large-scale datasets – Haq, Moradi, Wang Generating CT-scans with 3D Generative Adversarial Networks Using a Supercomputer – Ruhe, Codreanu, va Leeuwen, Podareanu, Saletore, Teuwen Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images – Gamper, Chan, Tsang, Snead, Rajpoot One-Click Spine MRI – De Goyeneche, Peterson, He, Addy, Santos Improved generalizability of deep-learning based low dose volumetric contrast-enhanced MRI – Tamir, Pasumarthi, Gong, Zaharchuk, Zhang Deep Recursive Bayesian Maximal Path for Fully Automatic Extraction of Coronary Arteries in CT Images – Jeon, Shim, Chang A Deep Multi-Modal Method for Patient Wound Healing Assessment – Oota, Rowtula, Mohammed, Galitz, Liu, Gupta Signal recovery with un-trained convolutional neural networks – Heckel On the Similarity of Deep Learning Representations Across Didactic and Adversarial Examples – Douglas, Farahani Generative Smoke Removal – Sidorov, Wang, Alaya-Chekh Towards High Fidelity Direct-Contrast Synthesis from Magnetic Resonance Fingerprinting – Wang, Karasan, Doneva, Tan HR-CAMs : Using multi-level features for precise discriminative localization of pathology – Ingalhalikar, Shinde, Chougule, Saini Towards Autism detection on brain structural MRI scans with Adversarially Learned Inference – Garcia Neural Network Compression using Reinforcement Learning in Medical Image Segmentation – Chhabra, Soni, Avinash

Niki Parmar, Haraldur Hallgrimsson, Christian Kames, Arijit Patra, Abdullah-Al-Zubaer Imran, Junlin Yang, David Zimmerer, Arunava Chakravarty, Lawrence Schobs, Alexej Gossmann, TUNG-I CHEN, Tarun Dutt, Li Yao, Octavio Eleazar Martinez Manzanera, Hans Pinckaers, Mehmet Ufuk Dalmis, Deepak Gupta, Nandinee F Haq, David Ruhe, Jevgenij Gamper, Alfredo De Goyeneche Macaya, Jon Tamir, Byunghwan Jeon, SUBBAREDDY OOTA, Reinhard Heckel, Pamela Douglas, Oleksii Sidorov, Ke Wang, Melanie Garcia, Ravi Soni, Ankita Shukla
Sat 4:15 p.m. - 5:00 p.m. [iCal]

AI and Radiology: How machine learning will change the way we see patients, and the way we see ourselves

Daniel Sodickson
Sat 5:00 p.m. - 6:00 p.m. [iCal]

3 Winner Talks of fastMRI

Nafissa Yakubova, Nicola Pezzotti, Puyang Wang, Larry Zitnick, Dimitrios Karkalousos, Shanhui Sun, Matthan Caan, Tullie Murrell, Patrick Putzky
Sat 6:00 p.m. - 6:05 p.m. [iCal]
Closing Remarks (Talks)

Author Information

Hervé Lombaert (ETS Montreal / Inria)

Hervé is Associate Professor at ETS Montreal, Canada and Affiliated Research Scientist at Inria, France - His research interests are in Statistics on Shapes, Data & Medical Images. He had the chance to work in multiple centers, including Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), Inria Sophia-Antipolis (France), McGill University (Canada), and Polytechnique Montreal (Canada). He is also a recipient of the François Erbsmann Prize, a top prize in Medical Image Analysis, earned a Best Thesis Award at Polytechnique Montreal, as well as several other prizes and fellowships - Hervé co-organized several workshops and special sessions in major international conferences, including NIPS and ICML, on Medical Image Analysis.

Ben Glocker (Imperial College London)
Ender Konukoglu (ETH Zurich)
Marleen de Bruijne (Erasmus MC/University of Copenhagen)
Aasa Feragen (University of Copenhagen, Denmark)
Ipek Oguz (Vanderbilt University)
Jonas Teuwen (Radboud University Medical Center / Netherlands Cancer Institute)

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