概化理论
计算机科学
一般化
正规化(语言学)
人工智能
领域(数学分析)
机器学习
最大值和最小值
模式识别(心理学)
统计
数学
数学分析
作者
Mohammad Atwany,Mohammad Yaqub
标识
DOI:10.1007/978-3-031-16434-7_61
摘要
Domain Generalization is a challenging problem in deep learning especially in medical image analysis because of the huge diversity between different datasets. Existing papers in the literature tend to optimize performance on single target domains, without regards to model generalizability on other domains or distributions. High discrepancy in the number of images and major domain shifts, can therefore cause single-source trained models to under-perform during testing. In this paper, we address the problem of domain generalization in Diabetic Retinopathy (DR) classification. The baseline for comparison is set as joint training on different datasets, followed by testing on each dataset individually. We therefore introduce a method that encourages seeking a flatter minima during training while imposing a regularization. This reduces gradient variance from different domains and therefore yields satisfactory results on out-of-domain DR classification. We show that adopting DR-appropriate augmentations enhances model performance and in-domain generalizability. By performing our evaluation on 4 open-source DR datasets, we show that the proposed domain generalization method outperforms separate and joint training strategies as well as well-established methods. Source Code is available at https://github.com/BioMedIA-MBZUAI/DRGen .
科研通智能强力驱动
Strongly Powered by AbleSci AI