English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiologyartificial intelligence (AI) that performs well with small neural networks andsmall training sets. We seek to realize this potential via a proof-of-principleapplication to MRI brain sequence classification.  Methods: We analyzed a training set of 20 patients, each with foursequences/weightings: T1, T1 post-contrast, T2, and T2-FLAIR. We trained theparameters of a relatively small convolutional neural network (CNN) as follows:First, we randomly mutated the CNN weights. We then measured the CNN trainingset accuracy, using the latter as the fitness evaluation metric. The fittestchild CNNs were identified. We incorporated their mutations into the parentCNN. This selectively mutated parent became the next generation's parent CNN.We repeated this process for approximately 50,000 generations.  Results: DNE achieved monotonic convergence to 100% training set accuracy.DNE also converged monotonically to 100% testing set accuracy.  Conclusions: DNE can achieve perfect accuracy with small training sets andsmall CNNs. Particularly when combined with Deep Reinforcement Learning, DNEmay provide a path forward in the quest to make radiology AI more human-like inits ability to learn. DNE may very well turn out to be a key component of themuch-anticipated meta-learning regime of radiology AI algorithms that can adaptto new tasks and new image types, similar to human radiologists.

Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification