Research Area:  Machine Learning

Abstract:

Current medical image synthetic augmentation techniques rely on the intensive use of generative adversarial networks (GANs). However, the nature of GAN architecture leads to heavy computational resources to produce synthetic images and the augmentation process requires multiple stages to complete. To address these challenges, we introduce a novel generative meta curriculum learning method that trains the task-specific model (student) end-to-end with only one additional teacher model. The teacher learns to generate curriculum to feed into the student model for data augmentation and guides the student to improve performance in a meta-learning style. In contrast to the generator and discriminator in GAN, which compete with each other, the teacher and student collaborate to improve the students performance on the target tasks. Extensive experiments on the histopathology datasets show that leveraging our framework results in significant and consistent improvements in classification performance.

Keywords:  
Metalearning
Image resolution
Histopathology
Computational modeling
Data augmentation
Generative adversarial networks
Generators

Author(s) Name:  Meng Li, Chaoyi Li, Can Peng

Publisher name:  IEEE

DOI:  10.1109/ICIP49359.2023.10222093

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/10222093/