  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.
