A Simple Normalization Technique Using Window Statistics to Improve the Out-of-Distribution Generalization on Medical Images.
IEEE Trans Med Imaging
; 43(6): 2086-2097, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38224511
ABSTRACT
Since data scarcity and data heterogeneity are prevailing for medical images, well-trained Convolutional Neural Networks (CNNs) using previous normalization methods may perform poorly when deployed to a new site. However, a reliable model for real-world clinical applications should generalize well both on in-distribution (IND) and out-of-distribution (OOD) data (e.g., the new site data). In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which offers a simple yet effective alternative to existing normalization methods. Specifically, WIN perturbs the normalizing statistics with the local statistics computed within a window. This feature-level augmentation technique regularizes the models well and improves their OOD generalization significantly. Leveraging its advantage, we propose a novel self-distillation method called WIN-WIN. WIN-WIN can be easily implemented with two forward passes and a consistency constraint, serving as a simple extension to existing methods. Extensive experimental results on various tasks (6 tasks) and datasets (24 datasets) demonstrate the generality and effectiveness of our methods.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Imagem Assistida por Computador
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Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article