Papers

We have accepted 18 papers to be included in the Volume 158 of the Proceedings of Machine Learning Research.

Rui Li*, Stephanie Hu*, Mingyu Lu, Yuria Utsumi, Prithwish Chakraborty, Daby M. Sow, Piyush Madan, Mohamed Ghalwash, Zach Shahn, Li-wei H. Lehman: G-Net: a Recurrent Network Approach to G-Computation for Counterfactual Prediction Under a Dynamic Treatment Regime

Jonathan Rubin, Ramon Erkamp, Ragha Srinivasa Naidu, Anumod Odungatta Thodiyil, Alvin Chen: Attention Distillation for Detection Transformers: Application to Real-Time Video Object Detection in Ultrasound

Esther Dietrich, Patrick Fuhlert, Anne Ernst, Guido Sauter, Maximilian Lennartz, H. Siegfried Stiehl, Marina Zimmermann*, Stefan Bonn*: Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network

Ajay Jaiswal, Liyan Tang, Meheli Ghosh, Justin F Rousseau, Yifan Peng, Ying Ding: RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification

Ilya Valmianski, Nave Frost, Navdeep Sood, Yang Wang, Baodong Liu, James J. Zhu, Sunil Karumuri, Ian M. Finn, Daniel S. Zisook: SmartTriage: A system for personalized patient data capture, documentation generation, and decision support

Mark Endo*, Rayan Krishnan*, Viswesh Krishna, Andrew Y. Ng, Pranav Rajpurkar: Retrieval-Based Chest X-Ray Report Generation Using a Pre-trained Contrastive Language-Image Model

Oliver Carr*, Avelino Javer*, Patrick Rockenschaub*, Owen Parsons, Robert Dürichen: Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes

Ho Danliang, Iain BH Tan, Mehul Motani: Prognosticating Colorectal Cancer Recurrence using an Interpretable Deep Multi-view Network

Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan: MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

Peiqi Wang, Ruizhi Liao, Daniel Moyer, Seth Berkowitz, Steven Horng, Polina Golland: Image Classification with Consistent Supporting Evidence

Tianwei Yin*, Zihui Wu*, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman: End-to-End Sequential Sampling and Reconstruction for MRI

Alexander Campbell*, Lorena Qendro*, Pietro Lio, Cecilia Mascolo: Early Exit Ensembles for Uncertainty Quantification

Seongsu Bae, Daeyoung Kim, Jiho Kim, Edward Choi: Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture

Chao Pang, Xinzhuo Jiang, Krishna S. Kalluri, Matthew Spotnitz, RuiJun Chen, Adler Perotte, Karthik Natarajan: CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks

Tuan Truong, Sadegh Mohammadi, Matthias Lenga: How Transferable are Self-supervised Features in Medical Image Classification Tasks?

Neeraj Wagh, Jionghao Wei, Samarth Rawal, Leland Barnard, Benjamin Brinkmann, Brent Berry, Gregory Worrell, David Jones, Yogatheesan Varatharajah: Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability

Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel: Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Bryan Gopal*, Ryan Han*, Gautham Raghupathi*, Andrew Ng, Geoff Tison**, Pranav Rajpurkar**: 3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations

Extended Abstracts

We have accepted 30 extended abstracts for presentation at the workshop, which are hosted on the ML4H 2021 arXiv index.

Melle B. Vessies*, Sharvaree P. Vadgama*, Rutger R. van de Leur, Pieter A. Doevendans, Rutger J. Hassink, Erik Bekkers, René van Es: Interpretable ECG classification via a query-based latent space traversal (qLST)

Lin Lawrence Guo, Stephen R Pfohl, Jason Fries, Alistair Johnson, Jose Posada, Catherine Aftandilian, Nigam Shah, Lillian Sung: Evaluation of Domain Generalization and Adaptation on Improving Model Robustness to Temporal Dataset Shift in Clinical Medicine

Candelaria Mosquera, Luciana Ferrer, Diego H. Milone, Daniel Luna, Enzo Ferrante: Understanding the impact of class imbalance on the performance of chest x-ray image classifiers

Adedolapo Aishat Toye, Suryaprakash Kompalli: Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease

Kyunghoon Hur*, Jiyoung Lee*, Jungwoo Oh, Wesley Price, Young-Hak Kim, Edward Choi: Unifying Heterogenous Electronic Health Records Systems via Text-Based Code Embedding

Sharmita Dey, Sabri Boughorbel, Arndt F. Schilling: Learning a Shared Model for Motorized Prosthetic Joints to Predict Ankle-Joint Motion

Neil Band*, Tim G. J. Rudner*, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal: Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks

Kevalee Shah, Dimitris Spathis, Chi I. Tang, Cecilia Mascolo: Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes

Newton Mwai Kinyanjui*, Fredrik D. Johansson*: ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects

Niall Taylor, Lei Sha, Dan W Joyce, Alejo Nevado-Holgado, Thomas Lukasiewicz, Andrey Kormilitzin: Rationale production to support clinical decision-making

Subhrajit Roy*, Diana Mincu*, Negar Rostamzadeh, Chintan Ghate, Nenad Tomasev, Jessica Schrouff, Natalie Harris, Christina Chen, Fletcher Lee Hartsell, Katherine Heller.: Disability prediction in multiple sclerosis using performance outcome measures and demographic data

Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck, Sarah C. Brueningk: Interpretability Aware Model Training to Improve Robustness against OOD Magnetic Resonance Images in Alzheimer's Disease Classification

Quentin Blampey, Mehdi Rahim: HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level Forecast

Eric Wu*, Kevin Wu*, James Zou: Explaining medical AI performance disparities across sites with confounder Shapley value analysis

Varun Nair*, Namit Katariya, Xavier Amatriain, Ilya Valmianski, Anitha Kannan: Adding more data does not always help: A study in medical conversation summarization with PEGASUS

Hassan Baker, Austin J. Brockmeier: Exploring latent networks in resting-state fMRI using voxel-to-voxel causal modeling feature selection

Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis: Multi network InfoMax: A pre-training method involving graph convolutional networks

Mathieu Godbout, Alexandre Lachance, Fares Antaki, Ali Dirani, Audrey Durand: Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data

Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis: Brain dynamics via Cumulative Auto-Regressive Self-Attention

Jingshu Liu*, Patricia J. Allen*, Luke Benz, Daniel Blickstein, Evon Okidi, Xiao Shi: A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design

Haoran Zhang, Natalie Dullerud, Karsten Roth, Stephen Pfohl, Marzyeh Ghassemi: Improving the Fairness of Deep Chest X-ray Classifiers

Roxana Daneshjou*, Kailas Vodrahalli*, Weixin Liang*, Roberto A Novoa, Melissa Jenkins, Veronica Rotemberg, Justin Ko, Susan M Swetter, Elizabeth E Bailey, Olivier Gevaert, Pritam Mukherjee, Michelle Phung, Kiana Yekrang, Bradley Fong, Rachna Sahasrabudhe, Albert Chiou, James Zou: Disparities in Dermatology AI: Assessments Using Diverse Clinical Images

Amirata Ghorbani, Andre Esteva, James Zou: Grading of Prostate Whole-slide Images Using Weak Self-supervised Learning

Kristina M. Holton*, Shi Yu Chan*, Austin J. Brockmeier, Dost Öngür, Mei-Hua Hallk: Exploring the influences of functional connectivity architecture on cortical thickness networks in patients with early psychosis

Odhran O'Donoghue, Paul Duckworth, Giuseppe Ughi, Linus Scheibenreif, Kia Khezeli, Adrienne Hoarfrost, Samuel Budd, Patrick Foley, Nicholas Chia, John Kalantari, Graham Mackintosh, Frank Soboczenski, Lauren Sanders: Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery

Tanish Tyagi*, Colin G. Magdamo*, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi,Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T.Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das: Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records

Karina Zadorozhny, Patrick Thoral, Paul Elbers, Giovanni Cinà: Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation

Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer: Monte Carlo dropout increases model repeatability

Negar Rostamzadeh, Subhrajit Roy, Diana Mincu, Andrew Smart, Lauren Wilcox, Mahima Pushkarna, Razvan Amironesei, Jessica Schrouff, Madeleine Elish, Nyalleng Moorosi, Berk Ustun, Noah Broesti, Katherine Heller: Specialized Healthsheet for Healthcare Datasets

D. Suo, N. Agarwal, W. Xia, X. Chen, U. Ghai, A. Yu, P. Gradu, K. Singh, C. Zhang, E. Minasyan, J. LaChance, T. Zajdel, M. Schottdorf, D. Cohen, E. Hazan: Machine Learning for Mechanical Ventilation Control