This dataset (and associated paper) is for training a neural network to accurately measure in the PLAX view the:
To do this we obtained expert labels for 4 points:
A neural network was trained to localise these 4 points from which the 3 measurements could be made.
Under review
This is a snapshot of the data and code used for this paper. You should use the "latest release" if you are training your own neural network. These snapshots are provided for reproducibility.
The dataset for model development is divided into train and tune (referred to as progress-monitoring in the paper) sets. There are 2056 images in this dataset. 1894 have all 4 points marked. In total there are 5724 labelled images in this dataset (as we train for all tasks simultaneously).
The dataset for model validation, which comprises of 100 echocardiograms is kept private for competition use.
The model used for the paper was training run 147, epoch 300.
A snapshot of the exact code used for the paper is provided for reproducibility. The latest version of the code is available on GitHub, where some additional code has been provided to make it easier to do inference on your own data.
We are grateful to the following institutions for funding and support
This research and open-access release of the has been conducted under:
Any questions Dr. Matthew Shun-Shin