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.

翻译：医学图像分类是计算机辅助诊断中最关键的任务之一。深度学习模型，特别是卷积神经网络，已通过自动化特征学习成功应用于医学图像疾病分类。然而，多样化的成像模态和临床病理特征使得构建泛化性强且稳健的分类模型面临挑战。为提升模型性能，我们提出了一种新颖的预训练方法——前向-前向对比学习（Forward Forward Contrastive Learning, FFCL），该方法在前向-前向算法的基础上结合对比学习框架（包括局部与全局对比）实现预训练。在胸部X光数据集上的实验结果表明，所提出的FFCL在肺炎分类任务中优于现有预训练模型（相比ImageNet预训练的ResNet-18提升3.69%准确率）。此外，充分的消融实验验证了FFCL中局部与全局对比预训练设计的有效性。