前向-前向对比学习
Forward-Forward Contrastive Learning
AI总结：
针对医学图像分类因成像模态和病理多样导致泛化性与鲁棒性不足的问题，提出结合前向-前向算法的局部+全局对比预训练方法FFCL，在肺炎分类任务上准确率超ImageNet预训练ResNet-18达3.69%。
摘要：
医学图像分类是计算机辅助诊断最重要的任务之一。得益于自动特征学习，深度学习模型尤其是卷积神经网络已成功用于从医学图像中进行疾病分类。然而，多样的成像模态和临床病理使得构建通用且鲁棒的分类模型颇具挑战。为提升模型性能，我们提出一种新型预训练方法，即Forward Forward Contrastive Learning（FFCL，前向-前向对比学习），该方法在局部和全局层面的对比学习框架中利用Forward-Forward Algorithm（前向-前向算法）。我们在胸部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（比ImageNet预训练的ResNet-18准确率高3.69%）。此外，大量消融实验验证了FFCL中独特的局部与全局对比预训练设计的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
