Deep Learning in Healthcare Summit, Boston 2017

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7+

Hours of content

30

Speakers

2

Interviews

Overview

The 3rd Global Deep Learning in Healthcare Summit took place in Boston on May 25-26 2017. Speakers included: Junshui Ma, Senior Principal Scientist at Merck; Muyinatu Bell, Assistant Professor at John Hopkins University; Fabian Schmich, Data Scientist from Roche; and Nick Furlotte, Senior Scientist at 23andMe.

Topics explored included: AI in Drug Development & Clinical Trials; Medical Imaging; E-Heatlh Data; and Trends and Opportunities in the Future Healthcare.

Featured Presentations

Watch our featured videos below for free. Scroll down to view the full library of presentations from the event and to purchase access to all the recorded sessions.

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Improving Image Classification with Generative Adversarial Networks

Michael Dietz, Founder at Waya.AI

Generative adversarial networks (GANs) are one of the most promising areas in deep learning research. In this presentation we will briefly introduce GANs and see how they can be used in the real world - specifically for unsupervised learning and synthesizing data sets. We will analyze their strengths, weaknesses, and...
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Robust Deep Image Embeddings for Drug Repurposing

Mason Victors, Lead Data Scientist at Recursion Pharmaceutical

At Recursion, we are developing one of the richest fluorescent microscopy image sets in the world using Deep Learning. Unlike many computer vision tasks ours is not one of classification or recognition, but rather is focused on learning abstract representations of thousands of experimental conditions in order to identify drugs...
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Predicting Molecule Activity with Its Structure: Is Deep Neural Network an Evolutionary Solution or Revolutionary One?

Junshui Ma, Senior Principal Scientist at Merck

Quantitative structure-activity relationship (QSAR) is a technique used in pharmaceutical industry to find out a molecule’s efficacy and safety from its molecule structures using computer models, instead of doing lab tests. A number of independent efforts, including ours, have showed that Deep Neural Network (DNN) can outperform existing QSAR methods....
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Full Presentation Library

PANEL: From Innovation to Implementation in Healthcare

Mike McCormick, Comet Labs; Daniel Golden, Arterys; Michael Sierra, LEO Science & Tech Hub; Gaurav Tuli, Boston Children's Hospital

Heart Sound Classification via Convolutional Neural Network

Saman Pavarneh, Senior Research Scientist at Philips Research

Not Just a Black Box: Interpretable Deep Learning for Genomics

Avanti Shrikumar, PhD student at Stanford University

Getting Machines to Think Less Hard

Teddy Cha, CEO at PulseData

Reducing the Tedium of Radiology with Cloud-based Deep Learning

Daniel Golden, Director of Machine Learning at Arterys

Developing Clinically Relevant Healthcare Solutions

Neil Tenenholtz, Senior Data Scientist at MGH & BWH Center for Clinical Data Science

PANEL: What Trends and Opportunities Can be Expected for the Future of Healthcare?

Hyuk-Jeen Suh, Samsung Ventures; "Dusty" Biswaroop Majumdar, IBM Watson Health; Anthony Chang, CHOC Children’s Orange County; Vijetha Vemulapalli, Berg; Michael Dahlweid, GE Healthcare

Dental CAD/CAM Automation with Deep Learning

Sergei Azernikov, Machine Learning Lead at Glidewell Laboratories

A Machine Learning Approach to Improve Photoacoustic-Guided Surgery

Muyinatu Bell, Assistant Professor at John Hopkins University

Automated Histopathologic Tissue Annotation with Deep Learning

Fabian Schmich, Data Scientist at Roche

Modular Diagnosis of Cervical Cancer Using Smartphone and AI

Daniel Luci, Lead AI Researcher at Alexapath

A Journey into Learning from Medical Records

Avi Shoshman, Head of Data Science at Medial EarlySign

How are you Feeling? Determining the Latent State of Individuals Engaged in Conversation

Tuka Alhanahi, PhD Candidate at MIT

Segmentation of Medical Images via Deep Learning Techniques

Jose Dolz, Postdoctoral Researcher at LIVIA

Activity Recognition Using Deep Convolutional Neural Networks

Brian Todd, Data Scientist at WHOOP

Continuous Clinical Event Prediction with Recurrent Deep Networks

Nathan Hunt, PhD Candidate at MIT

[Un]supervised Patient Characterization using Electronic Health Records

Hossein Eistiri, Research Fellow at Harvard Medical School

Modeling Industrial ADMET Data with Multitask Networks

Brian Goldman, Research Fellow at Vertex Pharmaceuticals

Neural Networks as a Basis for Genetics-Based Predictions

Nick Furlotte, Senior Scientist at 23andMe

Affinity Project - Deep Convolutional Neural Networks That Learn from 3D Images of Proteins

Maksym Korablyov, Researcher at MIT

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