Posters


We have accepted 97 short papers for poster presentation at the workshop.

These are listed below, with links to the paper on arXiv if provided by the authors.

These will appear at two possible poster sessions on Fri. Dec. 8 in Room 104A of Long Beach Convention Center:

Presenters: Remember to follow the posted poster instructions: Portrait format. Max size: 20 inches wide and 30 inches tall.

Poster Session 1 (10:20-10:50)

Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks

Amy Jin, Serena Yeung, Jeffrey Jopling, Jonathan Krause, Dan Azagury, Arnold Milstein, and Li Fei-Fei

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Stephanie Hyland, Cristóbal Esteban and Gunnar Rätsch

Ask the doctor -- Improving drug sensitivity predictions through active expert knowledge elicitation

Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski and Pekka Marttinen

Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care

Hyunwoo Lee, Jooyoung Kim, Dojun Yang and Joon-Ho Kim

Automated Knee X-ray Report Generation

Aydan Gasimova, Giovanni Montana and Daniel Rueckert

Mondrian Processes for Flow Cytometry Analysis

Disi Ji, Eric Nalisnick, Padhraic Smyth

Cross-Modal Data Programming for Medical Images

Nishith Khandwala, Alexander Ratner, Jared Dunnmon, Roger Goldman, Matthew Lungren, Daniel Rubin and Chris Re

CliNER 2.0: Accessible and Accurate Clinical Concept Extraction

Tristan Naumann, Willie Boag, Elena Sergeeva, Saurabh Kulshreshtha and Anna Rumshisky

A Public Benchmark for Clinical Prediction and Multitask Learning

Hrayr Harutyunyan, Hrant Khachatrian, David Kale, Greg Ver Steeg and Aram Galstyan

Finding Algebraic Structure of Care in Time: A Deep Learning Approach

Phuoc Nguyen, Truyen Tran and Svetha Venkatesh

Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets

Shashank Gupta, Manish Gupta, Vasudeva Varma, Sachin Pawar, Nitin Ramrakhiyani and Girish Keshav Palshikar

Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data

Petar Veličković, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, and Matthieu Vegreville

Predicting Adolescent Suicide Attempts with Neural Networks

Harish S. Bhat, Sidra J. Goldman-Mellor

Deep Multi-Instance Learning for Concept Annotation from Medical Time Series Data

Sanjay Purushotham, Zhengping Che, Bo Jiang and Yan Liu

A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure

Prithwish Chakraborty, Vishrawas Gopalakrishnan, Sharon M.H. Alford, and Faisal Farooq

Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention

Alican Bozkurt, Kivanc Kose, Jaume Coll-Font, Christi Alessi-Fox, Dana H. Brooks, Jennifer G. Dy, Milind Rajadhyaksha

Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes

Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler, Anna Druet, Hans Raffauf and Noémie Elhadad

Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression

Kelly Peterson, Ognjen (Oggi) Rudovic, Ricardo Guerrero, and Rosalind W. Picard

Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks

Joseph Futoma, Anthony Lin, Mark Sendak, Armando Bedoya, Meredith Clement, Cara O'Brien and Katherine Heller

Machine Learning-Based Risk Profile Classification: A Case Study for Heart Valve Surgery

Ulrich Bodenhofer, Bettina Haslinger-Eisterer, Alexander Minichmayer, Georg Hermanutz and Jens Meier

Image Segmentation to Distinguish Between Overlapping Human Chromosomes

R Lily Hu, Jeremy Karnowski, Ross Fadely and Jean-Patrick Pommier

Adversarial Networks for Prostate Cancer Detection

Simon Kohl, David Bonekamp, Heinz-Peter Schlemmer, Kaneschka Yaqubi, Markus Hohenfellner, Boris Hadaschik, Jan-Philipp Radtke, Klaus Maier-Hein

Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks

Thanh Hai Nguyen, Yann Chevaleyre, Edi Prifti, Nataliya Sokolovska and Jean-Daniel Zucker

On the importance of normative data in speech-based assessment

Zeinab Noorian, Chloé Pou-Prom, Frank Rudzicz

A Hierarchical Generative Model of Electrocardiogram Records

Andrew Miller, Sendhil Mullainathan and Ziad Obermeyer

Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets

Shashank Gupta, Manish Gupta, Vasudeva Varma, Sachin Pawar, Nitin Ramrakhiyani and Girish Keshav Palshikar

Viewpoint Invariant Convolutional Networks for Identifying Risky Hand Hygiene Scenarios

Michelle Guo, Albert Haque, Serena Yeung, Jeffrey Jopling, Lance Downing, Alexandre Alahi, Brandi Campbell, Kayla Deru, William Beninati, Arnold Milstein and Li Fei-Fei

Causality Refined Diagnostic Prediction

Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström

Deep Risk: Timely Risk Scoring by a Recurrent Ensemble of Recurrent Neural Networks

Anton Nemchenko, William Zame and Mihaela Van Der Schaar

Poster Session 2 (15:20-15:50)

Personalized Survival Prediction with Contextual Explanation Networks

Maruan Al-Shedivat, Avinava Dubey and Eric Xing

Vision-Based Prediction of ICU Mobility Care Activities using Recurrent Neural Networks

Gabriel M. Bianconi, Rishab Mehra, Serena Yeung, Francesca Salipur, Jeffrey Jopling, Lance Downing, Albert Haque, Alexandre Alahi, Brandi Campbell, Kayla Deru, William Beninati, Arnold Milstein and Li Fei-Fei

Deep Convolutional Networks for Detecting Arrhythmias from Pulsatile Signals

Ming-Zher Poh, Yukkee C. Poh, Chun-Ka Wong, Pak-Hei Chan and Chung-Wah Siu

Unsupervised Phenotype Scoring

Ioakeim Perros, Evangelos Papalexakis, Elizabeth Searles, Woosang Lim, Haesun Park and Jimeng Sun

Prediction-Constrained Topic Models for Antidepressant Recommendation

Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy, Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

Data Augmentation for Aortic Valve Morphology Classification from Phase-Contrast MRI

Ke Xiao, Heliodoro Tejeda Lemus, Madalina Fiterau, Jason Fries, Christopher Ré, Euan Ashley and James Priest

Improving Matrix Completion in Healthcare through Missingness Estimation

Carolyn Kim, Erika Strandberg and Mohsen Bayati

A multiobjective deep learning approach for predictive classification in Neuroblastoma

Valerio Maggio, Marco Chierici, Giuseppe Jurman, Cesare Furlanello

Deep Reinforcement Learning for Sepsis Treatment

Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, Marzyeh Ghassemi

A deep learning-based method for relative location prediction in CT scan images

Jiajia Guo, Hongwei Du , Bensheng Qiu and Xiao Liang

Predicting Severe Sepsis Using Text from the Electronic Health Record

Phil Culliton, Michael Levinson, Alice Ehresman RN, Joshua Wherry, Jay S. Steingrub MD, Stephen I. Gallant

Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events

Antoine Honore, Saikat Chatterjee, Veronica Siljehav and Eric Herlenius

An Encoder-Decoder Model for ICD-10 Coding of Death Certificates

Elena Tutubalina and Zulfat Miftahutdinov

Segmenting Brain Tumors with Symmetry

Hejia Zhang, Xia Zhu, and Theodore L. Willke

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease

Wolfgang Fruehwirt, Matthias Gerstgrasser, Pengfei Zhang, Leonard Weydemann, Markus Waser, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Dieter Grossegger, Heinrich Garn, Gareth W. Peters, Stephen Roberts, Georg Dorffner

Predicting readmission risk from doctors' notes

Erin Craig, Carlos Arias, David Gillman

Interpreting Comorbidity Groups via Risk Trajectories in the Health Record

Bharat Srikishan, Rajesh Ranganath and Noemie Elhadad

Detection-aided liver lesion segmentation using deep learning

MÍriam Bellver, Kevis-Kokitsi Maninis, Pont-Tuset Jordi, Xavier Gir„-I-Nieto, Jordi Torres and Luc Van Gool

Wrist Sensor Fusion Enables Robust Gait Quantification Across Walking Scenarios

Zeev Waks, Itzik Mazeh, Chen Admati, Michal Afek, Yonatan Dolan, and Avishai Wagner

Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes

Hasham Ul Haq, Rameel Ahmad, and Sibt Ul Hussain

Detection of Tooth caries in Bitewing Radiographs using Deep Learning

Muktabh Mayank Srivastava, Pratyush Kumar, Lalit Pradhan and Srikrishna Varadarajan

Highrisk Prediction from Electronic Medical Records via Deep Attention Networks

You Jin Kim, Yun-Geun Lee, Jeong Whun Kim, Jin Joo Park, Borim Ryu, and Jung-Woo Ha

Cost-Effective Active Learning for Melanoma Segmentation

Marc Gorriz, Axel Carlier, Emmanuel Faure and Xavier Giro-i-Nieto

Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction

Marc-Andre Schulz, Gael Varoquaux, Alexandre Gramfort, Bertrand Thirion and Danilo Bzdok

Modeling sepsis progression using hidden Markov models

Brenden Petersen, Michael Mayhew, Kalvin Ogbuefi, John Greene, Vincent Liu and Priyadip Ray

Modelling & Exploring Clinical Symptom Trajectories Post-Traumatic Brain Injury

Filip Dabek, Peter Hoover, Elizabeth Jimenez and Jesus Caban

Learning Physiological Decline via Random Structure Mortality Prediction Using Split RNNs

Matthew McDermott, Marzyeh Ghassemi, Nathan Hunt, Harini Suresh, Geeticka Chauhan, Tristan Naumann and Peter Szolovits

Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients

Wei-Hung Weng, Mingwu Gao, Ze He, Susu Yan and Peter Szolovits

Automated Training Set Generation for Aortic Valve Classification

Vincent Chen, Paroma Varma, Madalina Fiterau, Seung-Pyo Lee, James Priest and Christopher Ré