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

arXiv: https://arxiv.org/abs/2112.12990 · HTML: https://mlsanity.com/paper/2112.12990/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)
- [Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes](https://mlsanity.com/paper/2106.09812/) (2021)
- [Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images](https://mlsanity.com/paper/2008.02708/) (2020)
- [Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets](https://mlsanity.com/paper/2010.10763/) (2020)
- [Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets](https://mlsanity.com/paper/2012.13321/) (2020)
- [Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images](https://mlsanity.com/paper/2102.02895/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution](https://mlsanity.com/paper/2203.12853/) (2022)
- [Deep neuroevolution for limited, heterogeneous data: proof-of-concept application to Neuroblastoma brain metastasis using a small virtual pooled image collection](https://mlsanity.com/paper/2211.14499/) (2022)
- [Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices](https://mlsanity.com/paper/2211.14500/) (2022)
- [Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies](https://mlsanity.com/paper/2404.08853/) (2024)

## Abstract (arXiv)

Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiology artificial intelligence (AI) that performs well with small neural networks and small training sets. We seek to realize this potential via a proof-of-principle application to MRI brain sequence classification. Methods: We analyzed a training set of 20 patients, each with four sequences/weightings: T1, T1 post-contrast, T2, and T2-FLAIR. We trained the parameters of a relatively small convolutional neural network (CNN) as follows: First, we randomly mutated the CNN weights. We then measured the CNN training set accuracy, using the latter as the fitness evaluation metric. The fittest child CNNs were identified. We incorporated their mutations into the parent CNN. 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 and small CNNs. Particularly when combined with Deep Reinforcement Learning, DNE may provide a path forward in the quest to make radiology AI more human-like in its ability to learn. DNE may very well turn out to be a key component of the much-anticipated meta-learning regime of radiology AI algorithms that can adapt to new tasks and new image types, similar to human radiologists.