University of St. Andrews
This type of automated sorting would allow prioritisation of malignant slides so that pathologists can review them first and reduce the time to diagnosis for patients with cancer.
The final model was able to correctly detect 97% of malignant slides and correctly detect 90% of all slides.
The final model is in two stages. Firstly, the very large images are split into smaller patches and a deep learning model is trained to classify each patch as malignant or not.
Next, a second stage model combines the small patches back together and predicts a classification for the whole slide, this compensates for noise in the predictions of the first stage.
/Public Release. This material from the originating organization/author(s) might be of the point-in-time nature, and edited for clarity, style and length. Mirage.News does not take institutional positions or sides, and all views, positions, and conclusions expressed herein are solely those of the author(s).View in full here.
