Commit 0640b9d4 authored by jenia jitsev's avatar jenia jitsev

JJ: update dataset paper MILA

parent af59f0a9
......@@ -40,6 +40,16 @@ The COVID-19 pandemic continues to have a devastating effect on the health and w
- Data from Radiological Society of North America. RSNA pneumonia detection challenge.
- COVID-19 Image Data Collection: Prospective Predictions Are the Future
Joseph Paul Cohen, Paul Morrison, Lan Dao, Karsten Roth, Tim Q Duong, Marzyeh Ghassemi
Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment. Data can be accessed here:
Code for baselines:
##### Towards an Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images
* paper:
* using EfficientNet Architecture - evolved by Neural Architecture Search (see below for details)
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