Timezone: »

Machine Learning for Health (ML4H): What makes machine learning in medicine different?
Andrew Beam · Tristan Naumann · Brett Beaulieu-Jones · Irene Y Chen · Madalina Fiterau · Samuel Finlayson · Emily Alsentzer · Adrian Dalca · Matthew McDermott

Fri Dec 13 08:00 AM -- 06:40 PM (PST) @ West Ballroom A
Event URL: https://ml4health.github.io/ »

The goal of the NeurIPS 2019 Machine Learning for Health Workshop (ML4H) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. Attendees at this workshop can also expect to broaden their network of collaborators to include clinicians and machine learning researchers who are focused on solving some of the most import problems in medicine and healthcare. The organizers of this proposal have successfully run NeurIPS workshops in the past and are well-equipped to run this year’s workshop should this proposal be accepted.

This year’s theme of “What makes machine learning in medicine different?” aims to elucidate the obstacles that make the development of machine learning models for healthcare uniquely challenging. To speak to this theme, we have received commitments to speak from some of the leading researchers and physicians in this area. Below is a list of confirmed speakers who have agreed to participate.

Luke Oakden-Raynor, MBBS (Adelaide)
Russ Altman, MD/PhD (Stanford)
Lilly Peng, MD/PhD (Google)
Daphne Koller, PhD (in sitro)
Jeff Dean, PhD (Google)

Attendees at the workshop will gain an appreciation for problems that are unique to the application of machine learning for healthcare and a better understanding of how machine learning techniques may be leveraged to solve important clinical problems. This year’s workshop builds on the last two NeurIPS ML4H workshops, which were both attended by more than 500 people each year, and helped form the foundations of an emerging research community.

Please see the attached document for the full program.

Fri 8:45 a.m. - 9:15 a.m. [iCal]
Daphne Koller Talk (Keynote)
Fri 9:15 a.m. - 9:45 a.m. [iCal]

"Non-Invasive Silent Speech Recognition in Multiple Sclerosis with Dysphonia”, Arnav Kapur et al.

“Transfusion: Understanding Transfer Learning for Medical Imaging”, Maithra Raghu et al.

"Deep Survival Experts: A Fully Parametric Survival Regression Model”, Xinyu Li et al.

Arnav Kapur, Maithra Raghu, Xinyu Li
Fri 9:45 a.m. - 10:30 a.m. [iCal]
Coffee Break (NeurIPS coffee break)
Fri 10:30 a.m. - 11:00 a.m. [iCal]
Alan Karthikesalingam & Nenad Tomasev Talk (Presentation)
Nenad Tomasev
Fri 11:00 a.m. - 11:30 a.m. [iCal]

"Privacy Preserving Human Fall Detection using Video Data”, Umar Asif et al.

"Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection” Paul Jäger et al.

Umar Asif, Paul Jaeger
Fri 11:20 a.m. - 11:30 a.m. [iCal]
Message from sponsor (Presentation)
Max Baranov, Yuge Ji
Fri 11:30 a.m. - 12:30 p.m. [iCal]
Poster Session I (Poster session)
Shuangjia Zheng, Arnav Kapur, Umar Asif, Eyal Rozenberg, Cyprien Gilet, Oleksii Sidorov, Yogesh Kumar, Tom Van Steenkiste, Willie Boag, David Ouyang, Paul Jaeger, Sheng Liu, Aparna Balagopalan, Deepta Rajan, Marta Skreta, Nikhil Pattisapu, Jann Goschenhofer, Viraj Prabhu, Di Jin, Laura-Jayne Gardiner, Irene Z Li, sriram kumar, Isabelle Hu, Mehul Motani, Justin Lovelace, Usman Roshan, Lucy Lu Wang, Ilya Valmianski, Hyeonwoo Lee, Sunil Mallya, Elias Chaibub Neto, Jonas Kemp, Marie Charpignon, Amber Nigam, Wei-Hung Weng, Sabri Boughorbel, Alexis Bellot, Lovedeep Gondara, Haoran Zhang, Taha Bahadori, John Zech, Rulin Shao, Edward Choi, Laleh Seyyed-Kalantari, Emily Aiken, Ioana Bica, Yiqiu Shen, Kieran Chin-Cheong, Subhrajit Roy, Ioana Baldini, Tiffany Min, Dirk Deschrijver, Pekka Marttinen, Damian Pascual Ortiz, Supriya Nagesh, Niklas Rindtorff, Andriy Mulyar, Kathi Hoebel, Martha Shaka, Pierre Machart, Leon Gatys, Nathan Ng, Matthias Hüser, Devin Taylor, Dennis Barbour, Natalia L Martinez, Clara McCreery, Ben Eyre, Vivek Natarajan, Ren Yi, Ruibin Ma, Chirag Nagpal, Nan Du, Andy Gao, Anup Tuladhar, Sam Shleifer, Jason Ren, Pouria Mashouri, Max Lu, Farideh Bagherzadeh-Khiabani, Olivia Choudhury, Maithra Raghu, Scotty Fleming Fleming, Mika Jain, GUO YANG, Alena Harley, Stephen Pfohl, Elisabeth Rumetshofer, Alex Fedorov, Saloni Dash, Jacob Pfau, Sabina Tomkins, Colin Targonski, Mike Brudno, Xinyu Li, Yiyang Yu, Nisarg Patel
Fri 1:30 p.m. - 2:00 p.m. [iCal]

Models of Cognition: From Predicting Cognitive Impairment to the Brain Networks underlying Complex Cognitive Processes Talk

The ubiquity of smartphone usage in many people’s lives make it a rich source of information about a person’s mental and cognitive state. In this talk, we first consider how such data sources can be used to provide insights into an individual’s potential cognitive impairment.  Based on a study enriched with subjects diagnosed with mild cognitive impairment or Alzheimer's disease, we develop structured models of users’ smartphone interactions to reveal differences in phone usage patterns between people with and without cognitive impairment. In particular, we focus on inferring specific types of phone usage sessions that are predictive of cognitive impairment. Our model achieves state-of-the-art results when discriminating between healthy and symptomatic subjects, and its interpretability enables novel insights into which aspects of phone usage strongly relate with cognitive health in our dataset.

We then turn to a scientific analysis of brain functioning underlying cognitive behaviors. Recent neuroimaging modalities, such as magnetoencephalography (MEG), provide rich descriptions of brain activity over time enabling new studies of the neural underpinnings of complex cognitive processes. We focus on inferring the functional connectivity of auditory attention using MEG recordings.  We explore notions of undirected, contemporaneous interactions using sparse and interpretable deep generative models, as well as time-varying directed interactions using Bayesian dynamical models.

Emily Fox
Fri 2:00 p.m. - 2:30 p.m. [iCal]
Luke Oakden-Rayner Talk (Presentation)
Luke Oakden-Rayner
Fri 2:30 p.m. - 3:00 p.m. [iCal]

"Localization with Limited Annotation for Chest X-rays”, Eyal Rozenberg et al.

"Generative Image Translation for Data Augmentation in Colorectal Histopathology Images”, Jerry Wei et al.

"Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements”, Xiaojing Xu et al.

Eyal Rozenberg, Jerry Wei, Xiaojing Xu
Fri 3:00 p.m. - 3:30 p.m. [iCal]
Anna Goldenberg Talk (Presentation)
Anna Goldenberg
Fri 3:30 p.m. - 4:15 p.m. [iCal]
Coffee Break (NeurIPS coffee break)
Fri 4:15 p.m. - 4:45 p.m. [iCal]
Lily Peng & Dale Webster talk (Presentation)
Lily Peng
Fri 4:45 p.m. - 5:45 p.m. [iCal]
Panel Discussion (Luke Oakden-Rayner, Emily Fox, Alan Karthikesalingam, Daphne Koller, Anna Goldenberg) (Panel)

Author Information

Andrew Beam (Harvard)
Tristan Naumann (Microsoft Research)
Brett Beaulieu-Jones (Harvard Medical School)
Irene Y Chen (MIT)

Irene is a PhD student at MIT focusing on applications on health care and fairness. She did her undergrad at Harvard where I studied applied math and computational engineering. Before starting at MIT, she worked for two years at Dropbox as a data scientist and machine learning engineer.

Madalina Fiterau (CMU)
Sam Finlayson (Harvard Medical School)

Samuel Finlayson is a MD-PhD Candidate studying jointly at Harvard Medical School and Massachusetts Institute of Technology. His research focuses on developing machine learning methods for precision medicine. Current applications focus on neurological diseases and extend techniques from computer vision, natural language processing, and single-cell genomics. Previously, he studied Biomedical Informatics at Stanford University.

Emily Alsentzer (MIT)
Adrian Dalca (MIT, HMS)
Matthew McDermott (MIT)

More from the Same Authors