Machine Learning in Hematology: Reinventing the Blood Test

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

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

Deep learning for medical image quality control

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

Changing the Paradigm of Pathology: AI and Computational Diagnostics

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

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

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

3 Winner Talks of fastMRI