Self-supervised learning helps anticipate patient care based on lung X-ray-(COVID-19)

Facebook AI helps predict the care needed for patients COVID-19

In January 2021, Facebook AI researchers in collaboration with NYU Langone Health’s Predictive Analytics Unit and Department of Radiology, made available open source code for three models capable of predicting two types of deterioration in COVID-19 patients based on their chest X-ray: deterioration from adverse events (transfer to ICU, intubation, or mortality) and increased oxygen requirements beyond 6 L per day.

  • A prediction model of patient deterioration based on a sequence of radiographs.
  • A model to predict the amount of supplemental oxygen a patient may require based on a single x-ray.

Momentum Contrast MoCo

Facebook AI researchers, pre-trained a model called Momentum Contrast (MoCo) with two large public Chest x-ray datasets MIMIC-CXR-JPG and CheXpert.

Self-supervised learning

MoCo, the model used by Facebook AI, to anticipate the care of COVID-19 patients is a self-supervised learning model with a contrastive loss function.

Analogy the Black Forest cake by Y.Lecun

Contrastive methods for energy-based models

Self-supervised learning is based on energy-based models (EBM). These models can be theorized using an energy function F(x,y). If F(x,y) = 0, y is compatible with x and if F(x,y)>0 then y is not compatible with x.

  • Zj is the positive example, in my example another radio similar to the anchor with a white spot in the same place or the same image as the anchor but cropped differently.
  • Za is the negative example, in my example a radio with no spot.
  • When training: the product Zi.Zj should be pushed up and the product Zi.Za should be pushed down. To maximize the log and therefore minimize the contrastive cost function.

Go further Self-Supervised Learning

Go further Contrastive Learning with the vidéo of Yannic Kilcher

Diplodocus interested in the applications of artificial intelligence to healthcare. Twitter : @