MEDICAL IMAGING MEETS NeurIPS
Saturday, December 8th - Palais des Congrès de Montréal, Canada
Accepted Abstracts
Task adapted reconstruction for inverse problems. Jonas Adler (Royal Institute of Technology); Sebastian A Lunz (University of Cambridge); Olivier Verdier (KTH Royal Institute of Technology); Carola B Schoenlieb (Cambridge University); Ozan Öktem (Royal Institute of Technology),
Improving Skin Condition Classification with a Question Answering Model. Mohamed MA Akrout (University of Toronto); Amir-massoud Farahmand (Vector Institute); Tory Jarmain (Triage)
Coupling weak and strong supervision for classification of prostate cancer histopathology images. Eirini Arvaniti (ETH Zurich); Manfred Claassen (ETH Zurich)
Analyzing Alzheimer's Disease Progression from Sequential Magnetic Resonance Imaging Scans Using Deep 3D Convolutional Neural Networks. Sumana Basu (McGill University); Konrad Wagstyl (University of Cambridge); Azar Zandifar (McGill University); Louis Collins (McGill); Adriana Romero (FAIR); Doina Precup (McGill University)
Towards continual learning in medical imaging. Chaitanya Baweja (Imperial College London); Ben Glocker (Imperial College London); Konstantinos Kamnitsas (Imperial College London),
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network. Nathaniel Braman (Case Western Reserve University); David Beymer (IBM); Ehsan Dehghan Marvasti (IBM Research)
An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images. Shuai Chen (Erasmus MC Rotterdam); Marleen de Bruijne (Erasmus MC Rotterdam /University of Copenhagen)
Multi-Channel MR Image Reconstruction with Unsupervised Learning of Latent Coils. Joseph Cheng (Stanford University); John Pauly (Stanford University); Shreyas Vasanawala (Stanford University)
Improved Probabilistic Diffeomorphic Registration with CNNs. Adrian V Dalca (MIT); Guha Balakrishnan (MIT); John Guttag (MIT); Mert Sabuncu (Cornell)
Nonlinear adaptively learned optimization for object localization in 3D medical images. Bogdan Georgescu (Siemens Healthcare); Mayalen Etcheverry (Siemens Healthineers); Benjamin Odry (Siemens Healthineers); Thomas Re ( Siemens Healthineers); Shivam Kaushik (Siemens Healthineers); Bernhard Geiger (Siemens Healthineers); Mariappan Nadar ( Siemens Healthineers); Sasa Grbic (Siemens Healthineers); Dorin Comaniciu (Siemens Healthineers)
Graph Convolutions on Spectral Embeddings: Learning of Cortical Surface Data. Karthik Gopinath (ETS Montreal); Christian Desrosiers (ETS, Canada); Herve Lombaert (ETS Montreal / Inria)
Generative Bone Lesions Synthesis for Data Augmentation in X-ray. Anant Gupta (New York University); Sumit Chopra (Imagen Technologies); Christian Ledig (Imagen Technologies)
Learning to learn unlearned feature for brain tumor segmentation. Sungyeob Han (Seoul National University); Yeongmo Kim (Seoul National University); Seokhyeon Ha (Seoul National University); Jungwoo Lee (Seoul National University); Seunghong Choi (Seoul National University)
Constrained-CNN losses for weakly supervised segmentation. Hoel Kervadec (ETS); Jose Dolz (ETS); Ismail Ben Ayed (Canada)
Robotic surgical instrument segmentation using Dual Global Attention Upsample. Udaya Kumar (Manipal Institute of Technology); Vishal V (Manipal Institute of technology)
Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks. Siddique Latif (Information Technology University, Punjab, Pakistan and University of Southern Queensland, Australia ); Muhammad Asim (Information Technology University, Lahore); Muhammad Usman (COMSATS University Islamabad, Pakistan); Junaid Qadir (Information Technology University, Punjab, Pakistan); Rajib Rana (University of Southern Queensland, Australia )
Variational Level Set-based Deep Neural Networks in Medical Segmentation. Ngan Le (Carnegie Mellon University); Kha Gia Quach (Concordia University); Chi Nhan Duong (DeepCam); Khoa Luu (University of Arkansas); Marios Savvides (Carnegie Mellon University)
Full Volumetric Brain Tissue Segmentation in Non-Contrast CT using Memory Efficient Convolutional LSTMs. Sil C. van de Leemput (Radboud University Medical Center); Ajay Patel (Radboud UMC); Rashindra Manniesing (Radboud UMC)
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging. Xiaoxiao Li (Yale University); Vivek Singh (Siemens Healthineers); Yifan Wu (Siemens Healthineers); Klaus Kirchberg (Siemens Healthineers); James S Duncan (Yale University); Ankur Kapoor (Siemens Healthineers)
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumour. Raghav Mehta (McGill University); Tal Arbel (McGill)
Towards Fast Biomechanical Modeling of Soft Tissue Using Neural Networks. Felix Meister (Siemens Healthineers); Tiziano Passerini (Siemens Healthineers); Viorel Mihalef (Siemens Healthineers); Ahmet Tuysuzoglu (Siemens Healthineers); Andreas K Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Tommaso Mansi (Siemens Healthineers)
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation. Miguel A.B. Monteiro (Imperial College London); Mario Figueiredo (Instituto de Telecomunicações, IST, University of Lisbon); Arlindo Oliveira (INESC-ID)
Estimating Uncertainty in Neural Networks for Segmentation Quality Control. Matthew Ng (University of Toronto); Fumin Guo (Sunnybrook Research Institute); LaBonny Biswas (Sunnybrook Research Institute); Graham Wright (Sunnybrook Health Sciences)
DC-SegNet: A discretely constrained deep network for weakly supervised segmentation. Jizong Peng (ETS); Christian Desrosiers (ETS, Canada); Marco Pedersoli (École de technologie supérieure)
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. Christian S Perone (NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal); Pedro Ballester (PUCRS); Rodrigo C. Barros (PUCRS); Julien Cohen-Adad (NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal)
Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks. Meet Shah (Facebook AI Research); Yash Bhalgat (University of Michigan, Ann Arbor); Suyash P. Awate (Indian Institute of Technology (IIT) Bombay)
Prediction of Clinically Significant Prostate Cancer in MR/Ultrasound Guided Fusion Biopsy using Multiparametric MRI. Wen Shi (Xi'an Jiaotong University); Karthik V Sarma (UCLA); Alex Raman (UCLA); Alan Priester (UCLA); Shyam Natarajan (UCLA); William Speier (UCLA); Steven S. Raman (UCLA); Leonard Marks (UCLA); Corey Arnold (UCLA)
MR-Guided Blind PET Image Restoration Using Convolutional Neural Networks. Marzieh Tahaei (McGill Unviersity)
Prediction of Progression in Multiple Sclerosis Patients. Adrian Tousignant (Mcgill University); Doina Precup (McGill University); Tal Arbel (McGill)
Compressed Sensing Recovery of Medical Images using Deep Image Prior. David Van Veen (University of Texas at Austin); Ajil Jalal (UT Austin); Eric Price (University of Texas at Austin); Sriram Vishwanath (University of Texas at Austin); Alex Dimakis (UT Austin)
Reinforcement Learning for Online Sampling Optimization for Magnetic Resonance Imaging. David Y Zeng (Stanford University); Christopher M Sandino (Stanford University); Dwight Nishimura (Stanford University); Shreyas Vasanawala (Stanford University); Joseph Cheng (Stanford University)
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients. David Zimmerer (German Cancer Research Center (DKFZ); Jens Petersen (German Cancer Research Center (DKFZ); Simon Kohl (German Cancer Research Center); Klaus H. Maier-Hein (German Cancer Research Center (DKFZ))