Medical image classification is one of the most important tasks for
computer-aided diagnosis. Deep learning models, particularly convolutional
neural networks, have been successfully used for disease classification from
medical images, facilitated by automated feature learning. However, the diverse
imaging modalities and clinical pathology make it challenging to construct
generalized and robust classifications. Towards improving the model
performance, we propose a novel pretraining approach, namely Forward Forward
Contrastive Learning (FFCL), which leverages the Forward-Forward Algorithm in a
contrastive learning framework--both locally and globally. Our experimental
results on the chest X-ray dataset indicate that the proposed FFCL achieves
superior performance (3.69% accuracy over ImageNet pretrained ResNet-18) over
existing pretraining models in the pneumonia classification task. Moreover,
extensive ablation experiments support the particular local and global
contrastive pretraining design in FFCL.

该研究论文提出了一种基于前向前向对比学习的图像分类预训练方法，通过将前向算法与对比学习结合，可以构建出更加具有鲁棒性的分类模型，实验结果表明该方法可以取得比现有预训练模型更加出色的分类性能。