Scientific Program

Below you can find the preliminary program for MIDL 2018 in Amsterdam from July 4th – 6th. This is still subject to change.

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Wednesday July 4th

8:45 9:00 Opening remarks
9:00 9:40 Keynote: Dr. Graham Taylor, University of Guelph

Efficient techniques for learning confidence

Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. I will discuss a method of learning confidence estimates for neural networks that is simple to implement, computationally efficient and produces intuitively interpretable outputs. I will demonstrate that on the task of out-of-distribution detection, our technique surpasses recently proposed techniques which construct confidence based on the network’s output distribution, without requiring any additional labels or access to out-of-distribution examples. I will also show that it can generate per-pixel confidence maps and image-level prediction of failure in medical image segmentation.

9:40 10:40 Oral session I: Novel extensions to convolutional networks (1)
9:40 Cancer Metastasis Detection With Neural Conditional Random Field
Yi Li, Wei Ping; Baidu Silicon Valley Artificial Intelligence Lab, US
10:00 Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative
Felix Ambellan, Alexander Tack, Moritz Ehlke, Stefan Zachow, Zuse Institute Berlin, Germany
10:20 Robust training of recurrent neural networks to handle missing data for disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen, Biomediq A/S, Denmark
10:40 11:10 Break
11:10 12:30 Oral session II: Alternative DL architectures
11:10 OBELISK – One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis
Mattias P. Heinrich, Ozan Oktay, Nassim Bouteldja, University of Lübeck, Germany
11:30 3D G-CNNs for Pulmonary Nodule Detection
Marysia Winkels, Taco S. Cohen, University of Amsterdam/Aidence, the Netherlands
11:50 Capsules for Object Segmentation
Rodney LaLonde, Ulas Bagci, University of Central Florida, US
12:10 Convolutional neural networks for mesh-based parcellation of the cerebral cortex
Guillem Cucurull, Konrad Wagstyl, Arantxa Casanova, Petar Velickovic, Estrid Jakobsen, Michal Drozdzal, Adriana Romero, Alan Evans, Yoshua Bengio, Montreal Institute for Learning Algorithms, Canada
12:30 14:00 Lunch
14:00 14:40 Keynote: Prof. Ronald Summers, NIH

The impact of deep learning and artificial intelligence on radiology

Major advances in computer science and artificial intelligence, in particular “deep learning”, are beginning to have an impact on radiology. There has been an explosion of research interest and number of publications about the use of deep learning in radiology. In this presentation, I will show examples of how deep learning has led to major improvements in automated radiology image analysis, especially for image segmentation and computer aided diagnosis. I will also show how the radiology report can be used to do bulk annotation of images for training the deep learning systems.

14:40 16:15 Poster session I
Papers
Histopathology Stain-Color Normalization Using Generative Neural Networks
Farhad G. Zanjani, Svitlana Zinger, Peter H.N. de With, Babak E. Bejnordi, Jeroen A.W.M. van der Laak, Eindhoven University of Technology, the Netherlands
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Xiaoran Chen, Ender Konukoglu, ETH Zurich, Switzerland
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker, Imperial College London, UK
Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images
Zaneta Swiderska-Chadaj, Hans Pinckaers, Mart van Rijthoven, Maschenka Balkenhol,  Margarita Melnikova, Oscar Geessink, Quirine Manson, Geert Litjens, Jeroen van der Laak, Francesco Ciompi, Radboud University Medical Center, the Netherlands
Blood Vessel Geometry Synthesis using Generative Adversarial Networks
Jelmer M. Wolterink, Tim Leiner, Ivana Išgum, University Medical Center Utrecht, the Netherlands
Brain MRI super-resolution using 3D generative adversarial networks
Irina Sánchez, Verónica Vilaplana, Universitat Politècnica de Catalunya, Spain
RadBot-CXR: Classification of Four Clinical Finding Categories in Chest X-Ray Using Deep Learning
Chen Brestel, Ran Shadmi, Itamar Tamir, Michal Cohen-Sfaty, Eldad Elnekave, Zebra Medical Vision, Israel
Abstracts
A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology
Nathan Ing, Beatrice S. Knudsen, Arkadiusz Gertych, Jakub M. Tomczak, Max Welling, Cedars-Sinai Medical Center, US
Extraction of Airways using Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne, University of Copenhagen, Denmark
Subject-level Prediction of Segmentation Failure using Real-Time Convolutional Neural Nets
Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya V. Valindria, Mihir M. Sanghvi, Nay Aung, José Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kimm, Bernhard Kainz, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Daniel Rueckert, Ben Glocker, Imperial College London, UK
Using Three-Dimensional Cardiac Motion for Predicting Mortality in Pulmonary Hypertension: A Deep Learning Approach
Ghalib A. Bello, Timothy J.W. Dawes, Jinming Duan, Declan P. O’Regan, Imperial College London, UK
Automatic Shadow Detection in 2D Ultrasound
Qingjie Meng, Christian Baumgartner, Matthew Sinclair, James Housden, Martin Rajchl, Alberto Gomez, Benjamin Hou, Nicolas Toussaint, Jeremy Tan, Jacqueline Matthew, Daniel Rueckert, Julia Schnabel, Bernhard Kainz, Imperial College London, UK
Iteratively unveiling new regions of interest in Deep Learning models
Florian Bordes, Tess Berthier, Lisa Di Jorio, Pascal Vincent, Yoshua Bengio, Université de Montréal, Canada
Contextual Hourglass Networks for Segmentation and Density Estimation
Daniel Oñoro-Rubio, Mathias Niepert, NEC Labs Europe, Germany
TOMAAT: volumetric medical image analysis as a cloud service
Fausto Milletari, Johann Frei, Seyed-Ahmad Ahmadi, NVIDIA, US
Stereology as Weak Supervision for Medical Image Segmentation
Giorgia Silvestri, Luca Antiga, Orobix Srl, Italy
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
Wouter Bulten, Geert Litjens, Radboud University Medical Center, the Netherlands
Lung nodule segmentation with convolutional neural network trained by simple diameter information
Chang-Mo Nam, Jihang Kim, Kyong Joon Lee, Seoul National University Bundang Hospital, Republic of Korea
An ensemble of 3D convolutional neural networks for central vein detection in white matter lesions
Mário João Fartaria, Jonas Richiardi, João Jorge, Pietro Maggi, Pascal Sati, Daniel S. Reich, Reto Meuli, Cristina Granziera, Meritxell Bach Cuadra, Tobias Kober, ACIT SIEMENS, Switzerland
Learning-based solution to phase error correction in T2*-weighted GRE scans
Alexander Loktyushin, Philipp Ehses, Bernhard Schölkopf, Klaus Scheffler, MPI for Biological Cybernetics, Germany
Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks
Jim Winkens, Jasper Linmans, Bastiaan S. Veeling, Taco S. Cohen, Max Welling, University of Amsterdam, the Netherlands
16:15 19:00 Free time
19:00 21:00 Reception and Welcome Dinner

Thursday July 5th

9:00 9:40 Keynote: Dr. Adriana Romero, Facebook AI Research

Deep learning for genomics and graph-structured data

In the recent years, deep learning has achieved promising results in medical imaging analysis. However, in order to fully exploit the richness of healthcare data, new models able to deal with a variety of modalities have to be designed. In this talk, I will discuss recent advances in deep learning for genomics and graph-structured data. I will present Diet Networks, a recent contribution which copes with the high dimensionality of genomic data. Then, I will introduce our work on Graph Attention Networks, which has recently shown to improve results on protein-protein interaction networks and mesh-based parcellation of the cerebral cortex.

9:40 10:40 Oral session III: Novel extensions to convolutional networks (2)
09:40 MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
Simon Graham, Hao Chen, Qi Dou, Pheng Ann-Heng, Nasir Rajpoot, University of Warwick, UK
10:00 Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert, Imperial College London, UK
10:20 Iterative fully convolutional neural networks for automatic vertebra segmentation
Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, Ivana Isgum, University Medical Center Utrecht, the Netherlands
10:40 11:10 Break
11:10 12:30 Oral session IV: Medical image enhancement, generation and reconstruction
11:10 Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
Daniele Ravì, Agnieszka Barbara Szczotka, Dzhoshkun Ismail Shakir, Stephen P Pereira, Tom Vercauteren, University College London, UK
11:30 Temporal Interpolation via Motion Field Prediction
Lin Zhang, Neerav Karani, Christine Tanner, Ender Konukoglu, ETH Zurich, Switzerland
11:50 Stacked Bidirectional Convolutional LSTMs for 3D Non-contrast CT Reconstruction from Spatiotemporal 4D CT
Sil C. van de Leemput, Mathias Prokop, Bram van Ginneken, Rashindra Manniesing, Radboud University Medical Center, the Netherlands
12:10 Recurrent Inference Machines for Accelerated MRI Reconstruction
Kai Lønning, Patrick Putzky, Matthan Caan, Max Welling, University of Amsterdam, the Netherlands
12:30 14:00 Lunch
14:00 14:40 Keynote: Dr. Tim Salimans, Open AI

Detecting lung nodules using deep learning

Lung cancer is the leading cause of cancer-related death worldwide. By screening high risk individuals for lung nodules using low-dose CT scans, this type of cancer can be detected when it is still treatable. However, large-scale implementation of such screening programs requires radiologists to evaluate a huge number of scans, which is costly and error-prone. Aidence is an Amsterdam start-up developing an AI assistant for helping radiologists with detecting, reporting and tracking of lung nodules. This talk covers the deep learning techniques that we use to obtain state of the art accuracy in this domain, as well as the requirements and challenges faced when developing a deep learning system for use in clinical practice.

14:40 16:15 Poster session II
Papers
One-class Gaussian process regressor for quality assessment of transperineal ultrasound images
Saskia M. Camps, Tim Houben, Davide Fontanarosa, Christopher Edwards, Maria Antico, Matteo Dunnhofer, Esther G.H.J. Martens, Jose A. Baeza, Ben G.L. Vanneste, Evert J. van Limbergen, Peter H.N. de With, Frank Verhaegen, Gustavo Carneiro, Eindhoven University of Technology, the Netherlands
Unsupervised Deformable Image Registration with Fully Connected Generative Neural Network
Ameneh Sheikhjafari, Kumaradevan Punithakumar, Nilanjan Ray, University of Alberta, Canada
Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Alain Jungo, Raphael Meier, Ekin Ermis, Evelyn Herrman, Mauricio Reyes, University of Bern, Switzerland
Automatic multi-organ segmentation in dual energy CT using 3D fully convolutional network
Shuqing Chen, Xia Zhong, Shiyang Hu, Sabrina Dorn, Marc Kachelrieß, Michael Lell, Andreas Maier, Friedrich-Alexander-Universität, Germany
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Priya Lakshmi Narayanan, Andrew Dodson, Barry Guesterson, Mitchell Dowsett, Yinyin Yuan, Institute of Cancer Research, UK
Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
Xin Yi, Scott Adams, Paul Babyn, Abdul Elnajmi, University of Saskatchewan, Canada
CNN-based Landmark Detection in Cardiac CTA Scans
Julia M.H. Noothout, Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner, Ivana Išgum, University Medical Center Utrecht, the Netherlands
Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion
Ben A. Duffy, Wenlu Zhang, Haoteng Tang, Lu Zhao, Meng Law, Arthur W. Toga, Hosung Kim, USC Stevens Neuroimaging and Informatics Institute, US
Abstracts
A Deep Learning Framework for Automatic Diagnosis in Lung Cancer
Nikolay Burlutskiy, Feng Gu, Lena Kajland Wilen, Max Backman, Patrick Micke, ContextVision AB, Sweden
Deep Pose Estimation for Image-Based Registration
Benjamin Hou, Nina Miolane , Bishesh Khanal, Matthew C.H. Lee, Amir Alansary, Steven McDonagh, Joseph Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz, Imperial College London, UK
Image-Based Registration in Canonical Atlas Space
Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz, Imperial College London, UK
Standard Plane Localisation in 3D Fetal Ultrasound Using Network with Geometric and Image Loss
Yuanwei Li, Juan J. Cerrolaza, Matthew Sinclair, Benjamin Hou, Amir Alansary, Bishesh Khanal, Jacqueline Matthew, Bernhard Kainz, Daniel Rueckert, Imperial College London, UK
Deep Learning Methods for Estimating “Brain Age” from Structural MRI Scans
Sebastian G. Popescu, James H. Cole, Ben Glocker, David J. Sharp, Imperial College London, UK
Compact feature representations for human brain cytoarchitecture using self-supervised learning
Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheidm, Research Center Jülich, Germany
How to Cure Cancer (in images) with Unpaired Image Translation
Joseph Paul Cohen, Margaux Luck, Sina Honari, University of Montreal, Canada
You Only Look on Lymphocytes Once
Mart van Rijthoven, Zaneta Swiderska-Chadaj, Katja Seeliger, Jeroen van der Laak, Francesco Ciompi, Radboud University Medical Centre, the Netherlands
Improving weakly-supervised lesion localization with iterative saliency map refinement
Cristina González-Gonzalo, Bart Liefers, Bram van Ginneken, Clara I. Sánchez, Radboud University Medical Centre, the Netherlands
Quality control in radiotherapy-treatment planning using multi-task learning and uncertainty estimation
Felix J.S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen, Wenqi Li, David J. Hawkes, Sébastien Ourselin, Daniel C. Alexander, Jamie R. McClelland, M. Jorge Cardoso, University College London, UK
Monte-Carlo Sampling applied to Multiple Instance Learning for Whole Slide Image Classification
Marc Combalia, Verónica Vilaplana, Universitat Politècnica de Catalunya, Barcelona, Spain
Data-efficient Convolutional Neural Networks for Treatment Decision Support in Acute Ischemic Stroke
Adam Hilbert, Bastiaan S. Veeling, Henk A. Marquering, University of Amsterdam, the Netherlands
16:30 19:00 Drinks @ Aidence
Aidence is hosting a social event in their office (Keizersgracht 477E, Amsterdam) where MIDL participants can meet their team and get a feel of what startup life looks like. Prior registration is required as the number of guests is limited: Register here!

Friday July 6th

9:00 10:00 Oral session V: Uncertainty estimation and reinforcement learning in medical imaging
9:00 Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection
Amir Alansary, Ozan Oktay, Yuanwei Li, Loic Le Folgoc, Benjamin Hou, Ghislain Vaillant, Ben Glocker, Bernhard Kainz, Daniel Rueckert, Imperial College London, UK
9:20 Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation
Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, Myunghee Cho Paik, Seoul National University, Korea
9:40 Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks
Murat Seçkin Ayhan, Philipp Berens, University of Tübingen, Germany
10:00 11:30 Poster session III
Papers
Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound
Matthew D. Sinclair, Juan Cerrolaza Martinez, Emily Skelton, Yuanwei Li, Christian F. Baumgartner, Wenjia Bai, Jacqueline Matthew, Caroline L. Knight, Sandra Smith, Jo Hajnal, Andrew P. King, Bernhard Kainz, Daniel Rueckert, Imperial College London, UK
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
Jo Schlemper, Ozan Oktay, Liang Chen, Jacqueline Matthew, Caroline Knight, Bernhard Kainz, Ben Glocker,  Daniel Rueckert, Imperial College London, UK
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker, Imperial College London, UK
Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
Sahin Olut, Yusuf H. Sahin, Ugur Demir, Gozde Unal, Istanbul Technical University, Turkey
Towards Deep Cellular Phenotyping in Placental Histology
Michael Ferlaino, Craig A. Glastonbury, Carolina Motta-Mejia, Manu Vatish, Ingrid Granne, Stephen Kennedy, Cecilia M. Lindgren, Christoffer Nellåker, University of Oxford, UK
Motion Estimation in Coronary CT Angiography Images using Convolutional Neural Networks
Tanja Elss, Hannes Nickisch, Tobias Wissel, Rolf Bippus, Michael Morlock, Michael Grass, Philips Research Hamburg, Germany
Predicting Lesion Growth and Patient Survival in Colorectal Cancer Patients using Deep Neural Networks
Alexander Katzmann, Alexander Mühlberg, Michael Sühling, Dominik Nörenberg, Julian Walter Holch, Volker Heinemann, Horst-Michael Groß, Siemens Healthcare GmbH, Germany
MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar, Jeremy Irvin,  Aarti Bagul, Daisy Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L. Ball, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng, Stanford University, US
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
M. Orbes-Arteaga, Akshay Pai, Lauge Sørensen, Marc Modat, M. Jorge Cardoso, Sébastien Ourselin, Mads Nielsen, University College London, UK
Predictive Image Regression for Longitudinal Studies with Missing Data
Sharmin Pathan, Yi Hong, University of Georgia, US
Automatic Detection and Characterization of Coronary Artery Plaque and Stenosis using a Recurrent Convolutional Neural Network in Coronary CT Angiography
Majd Zreik, Robbert W. van Hamersvelt, Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum, University Medical Center Utrecht, the Netherlands
Abstracts
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis
Erik J. Bekkers, Maxime W. Lafarge, Mitko Veta, Koen A.J. Eppenhof, Josien P.W. Pluim, Remco Duits, Eindhoven University of Technology, the Netherlands
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification
Nils Gessert, Markus Heyder, Sarah Latus, David M. Leistner, Youssef S. Abdelwahed, Matthias Lutz, Alexander Schlaefer, Hamburg University of Technology, Germany
Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders
Nick Pawlowski, Matthew C.H. Lee, Martin Rajchl, Steven McDonagh, Enzo Ferrante, Konstantinos Kamnitsas, Sam Cooke, Susan Stevenson, Aneesh Khetani, Tom Newman, Fred Zeiler, Richard Digby, Jonathan P. Coles, Daniel Rueckert, David K. Menon, Virginia F.J. Newcombe, Ben Glocker, Imperial College London, UK
Comparison of deep learning-based techniques for organ segmentation in abdominal CT images
V. Groza, T. Brosch , D. Eschweiler, H. Schulz, S. Renisch, H. Nickisch, Philips Innovation Labs RUS, Russia
Gigapixel Whole-Slide Image Classification Using Unsupervised Image Compression And Contrastive Training
David Tellez, Jeroen van der Laak, Francesco Ciompi, Radboud University Medical Centre, the Netherlands
Training convolutional neural networks with megapixel images
J.H.F.M. Pinckaers, G.J.S Litjens, Radboud University Medical Centre, the Netherlands
Diagnosis of Maxillary Sinusitis on Waters’ View Conventional Radiograph using Convolutional Neural Network
Youngjune Kim, Kyong Joon Lee, Leonard Sunwoo, Dongjun Choi, Chang-Mo Nam, Jung Hyun Park, Seoul National University Bundang Hospital, Republic of Korea
Improving Data Augmentation for Medical Image Segmentation
Zach Eaton-Rosen, Felix Bragman, Sebastien Ourselin, M. Jorge Cardoso, University College London, UK
Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach
Jakub M. Tomczak, Maximilian Ilse, Max Welling, Marnix Jansen, Helen G. Coleman, Marit Lucas, Kikki de Laat, Martijn de Bruin, Henk Marquering, Myrtle J. van der Wel, Onno J. de Boer, C. Dilara Savci Heijink, Sybren L. Meijer, University of Amsterdam, the Netherlands
11:00 11:30 Break
11:30 12:10 Panel discussion
12:10 13:30 Lunch
13:30 14:50 Oral session VI: Weakly supervised and unsupervised learning in medical imaging
13:30 Deep Multi-Class Segmentation Without Ground-Truth Labels
Thomas Joyce, Agisilaos Chartsias, Sotirios A. Tsaftaris, University of Edinburgh, UK
13:50 Size-constraint loss for weakly supervised CNN segmentation
Hoel Kervadec, Jose Dolz, Meng Tang, Éric Granger, Yuri Boykov, Ismail Ben Ayed, ÉTS Montréal, Canada
14:10 Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification
Xi Wang, Hao Chen, Caixia Gan, Huangjing Lin, Qi Dou, Qitao Huang, Muyan Cai, Pheng-Ann Heng, The Chinese University of Hong Kong, China
14:30 Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion screening
Zara Alaverdyan, Julien Jung, Romain Bouet, Carole Lartizien, University Lyon, France
14:50 15:10 Closing and awards ceremony
15:10 Demo session and drinks
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