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Dina Demner-Fushman

Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

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Page 1: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

Dina Demner-Fushman

Page 2: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

Enhance users’ search experience

Provide direct access to relevant images from image databases and literature

Improve relevance of biomedical literature search results by targeting the visual content in articles

Find information relevant to a patient’s medical case

Page 3: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

OPEN-I USER INTERFACE

https://openi.nlm.nih.gov/

Page 4: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

OPEN-I DATA SOURCES The Open Access Subset of PubMed Central (PMC)

Articles: 1,139,338

Images: 3,707,654

Chest X-rays from the Indiana University hospital network (CXR) Radiology reports: 3,955

Images: 7,470

USC Digital Library Orthopedic Surgical Anatomy Teaching Collection (USC) Images: 2,064

Images from the NLM History of Medicine Division (HMD) Images: 67,517

MedPix Cases: 1,761

Images: 8,084

Page 5: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

Number of images

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

2011 2012 2013 2014 2015 2016

MedPix

HMD

USC

CXR

PMC

3,792,789

OPEN-I DATA GROWTH

Page 6: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in
Page 7: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

Image Processing

Text Processing

BOTTOM LINE

EXTRACTION

PANEL

SEGMENTATION

UMLS TERM

EXTRACTION

SUMMARY

GENERATION

IMAGE TYPE

DETECTION

IMAGE FEATURE

EXTRACTION

PREPARING CITATIONS FOR INDEXING

Page 8: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

PubMed citation

Automatically generated summary

Mention

Multi-panel figure &

caption segmentation

CITATIONS ENRICHED WITH TEXT AND IMAGES

Page 9: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

SUMMARY GENERATION

Problem: 50,000 articles in the open access subset of PMC have no abstracts.

Solution: automatic summarization

Bottom-line extractor + key-phrase extractor

Outperforms the best individual summarizer by 35%

Top 10 sentences in the order of occurrence in the article

Key-phrase extractor

KEA to identify most salient words

Outperformed an alternative key-phrase extractor by 25%

Sentences with high density of distinct salient words

Chachra S, Ben Abacha A, Demner-Fushman D. A Hybrid Approach to

Generation of Missing Abstracts in Biomedical Literature. COLING 2016

Page 10: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

OPEN SUMMARIZER: SUMMARY EXTRACTION AVAILABLE AS SERVICE

Flood events, OWA, risk preference, risk attitudes, flood hazard assessment, fuzzy AHP

Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference.

https://openi.nlm.nih.gov/summarizer.php

Page 11: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

MedPix provides 830 CME Teaching File Cases

Developed case submission server

For submission, editing and publication of case reports to Open-i and MedPix

MedPix – system for teaching

clinical imaging developed by

Dr. James Smirniotopoulos

Page 12: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

NICHD Placental Atlas Tool

The Placental Atlas Tool provides access to a comprehensive, centralized placental knowledge base, analytics tools, relevant research, publications, and resources.

Note: This is a mock up and the PAT system won’t be live until the end of the calendar year.

Page 13: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

An Image Explorer displays thumbnails of Open-i images and relevant metadata, linked to any publications NICHD have in the system, and linked back to the Open-i detailed view page.

Page 14: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

Dermatology collection

Rodney LongClem McDonald

Page 15: Open-I updates In response to BSC recommendationsOpen-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs) Deep Learning (CNN, GoogLeNet) second best in

ImageCLEFcaption 2017 evaluation (Asma Ben Abacha)

Concept detection

Caption prediction

Open-i-based approach ranked first over all runs in the challenge (11 groups, 71 runs)

Deep Learning (CNN, GoogLeNet) second best in the ranking of runs without the use of external resources

Started exploring Visual Question Answering based on MedPix /Open-i (Jason Lau)


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