Abstract: Computational methods and systems for training Artificial Intelligence (AI) models with improved translatability or generalisability (robustness) comprises training a plurality of Artificial Intelligence (AI) models using a common validation dataset over a plurality of epochs. During training of each model, at least one confidence metric is calculated at one or more epochs, and, for each model, the best confidence metric value over the plurality of epochs, and the associated epoch number at the best confidence metric is stored. An AI model is then generated by selecting at least one of the plurality of trained AI models based on the stored best confidence metric and calculating a confidence metric for the selected at least one trained AI model applied to a blind test set. The resultant AI model is saved and deployed if the best confidence metric exceeds an acceptance threshold.

Abstract: A decentralized training platform is described for training an Artificial Intelligence (AI) model where training data (e.g., medical images) is distributed across multiple sites (nodes) and due to confidentiality, legal, or other reasons the data at each site is unable to be shared or leave the site and so cannot be copied to a central location for training. The method comprises training a teacher model locally at each node and then moving each of the teacher models to a central node and using these to train a student model using a transfer dataset. This may be facilitated by setting up the cloud service using inter-region peering connections between the nodes to make the nodes appear as a single cluster. In one variation the student module may be trained at each node using the multiple trained teacher models.

Abstract: An Artificial Intelligence (AI) based computational system is used to non-invasively estimate the presence of a range of aneuploidies and mosaicism in an image of embryo prior to implantation. Aneuploidies and mosaicism with similar risks of adverse outcomes are grouped and training images are labelled with their group. Separate AI models are trained for each group using the same training dataset and the separate models are then combined, such as by using an Ensemble or Distillation approach to develop a model that can identify a wide range of aneuploidy and mosaicism risks. The AI model for a group is generated by training multiple models including binary models, hierarchical layered models and a multi-class model. In particular the hierarchical layered models are generated by assigning quality labels to images. At each layer the training set is partitioned in the best quality images and other images.