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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
Author Information
Niki Parmar (Google)
Haraldur Hallgrimsson (UC Santa Barbara)
Christian Kames (University of British Columbia)
Arijit Patra (University of Oxford)
Abdullah-Al-Zubaer Imran (University of California, Los Angeles)
Junlin Yang (Yale University)
David Zimmerer (German Cancer Research Center (DKFZ))
Arunava Chakravarty (Indian Institute of Technology Kharagpur, India)
Google Scholar: https://scholar.google.co.in/citations?user=Tk2svbAAAAAJ&hl=en Research Group: https://iitkliv.github.io/ https://researchweb.iiit.ac.in/~arunava.chakravarty/
Lawrence Schobs (University of Sheffield)
Alexej Gossmann (U.S. Food and Drug Administration)
TUNG-I CHEN (National Taiwan University)
Tarun Dutt (IIIT Bangalore)
Li Yao (Enlitic)
Octavio Eleazar Martinez Manzanera (King's College London)
Johannes Pinckaers (Radboud University Medical Center)
Hans Pinckaers is a PhD candidate in the Computational Pathology group of the Department of Pathology of the Radboud University Medical Center in Nijmegen. He studied Medicine at Leiden University Medical Center for which he obtained his MD in 2016. After his graduation he worked one year as a Pathology resident. In 2017, Hans joined the Diagnostic Image Analysis Group where he works under the supervision of Geert Litjens and Jeroen van der Laak on deep learning for improved prognostics in prostate cancer.
Mehmet Ufuk Dalmis (ScreenPoint Medical)
Deepak Gupta (University of Amsterdam)
Nandinee Haq (University of British Columbia)
I am a Research Scientist working on machine learning applications for grid resilience and sustainability.
David Ruhe (SurfSARA)
Jevgenij Gamper (University of Warwick, Cervest Ltd.)
I am a PhD student at Warwick Machine Learning Group and Tissue Image Analytics lab, and a Statistical Scientist at Cervest. My research interests are in model generalisation, and automatic machine learning for medical imaging and earth science. I have completed an M.Sc. in Mathematics of Systems. As part of the final curriculum, I worked on machine learning tools for exo-planet validation.
Alfredo De Goyeneche Macaya (Heartvista Inc.)
Jonathan Tamir (University of California, Berkeley)
Byunghwan Jeon (Yonsei University)
SUBBAREDDY OOTA (IIIT Hyderabad)
Reinhard Heckel (TUM)
Pamela K Douglas (UCLA/UCF)
cognitive computational neuroscientist
Oleksii Sidorov (Facebook)
Ke Wang (University of California, Berkeley)
I'm a second-year Ph.D. student in EECS at Berkeley, working on medical imaging. I'm broadly interested in magnetic resonance imaging, medical imaging, and computer vision.
Melanie Garcia (Institut hypercube)
Ravi Soni (GE Healthcare)
Ankita Shukla (IIIT -Delhi)
I am PhD student, working in Machine Learning and Computer Vision. My PhD work is to use Geometric and Semantic Constraints for Representation Learning. Both machine learning and deep learning algorithms can be improved by using geometric structure inherent in the data and the models. My application domain includes Visual Wildlife Monitoring application as well.
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