Congratulations to the 16 papers below selected for spotlight presentations!

These will be presented in rapid succession (2 min. each) during the 9:45 - 10:15 slot of our workshop program to give our audience a taste of exciting work happening in the ML+Health space.

Model-Based Reinforcement Learning for Sepsis Treatment

Aniruddh Raghu, Matthieu Komorowski and Sumeetpal Singh

Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential

Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali and Linwei Wang

Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network

Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng, Chi-Wen Huang, Tsung-Ying Ho, Chen-Kan Tseng, Le Lu and Min Sun

DeepSPINE: automated lumbar spinal stenosis grading using deep learning

Jen-Tang Lu, Stefano Pedemonte, Bernardo Bizzo, Sean Doyle, Katherine Andriole, Mark Michalski, R, Gilberto Gonzalez and Stuart Pomerantz

Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein and Li Fei-Fei

Deep Learning with Attention to Predict Gestational Age of the Fetal Brain

Liyue Shen, Edward Lee, Katie Shpanskaya and Kristen Yeom

Registration of Sparse Clinical Images

Kathleen Lewis, Guha Balakrishnan, John Guttag and Adrian Dalca

Improving Clinical Predictions through Unsupervised Time Series Representation Learning

Xinrui Lyu, Matthias Hüser, Stephanie Hyland, George Zerveas and Gunnar Rätsch

Multiple Instance Learning for ECG Risk Stratification

Divya Shanmugam, Davis Blalock and John Guttag

Inferring Multidimensional Rates of Aging from Cross-Sectional Data

Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson and Percy Liang

Using permutations to assess confounding in machine learning applications for digital health

Elias Chaibub Neto, Abhishek Pratap, Thanneer Perumal, Meghasyam Tummalacherla, Brian Bot, Lara Mangravite and Larsson Omberg

Deep Sequence Modeling for Hemorrhage Diagnosis

Fabian Falck, Michael Pinsky and Artur Dubrawski