First, our proposed PPT model introduces FocalNCE loss in patch-wise bidirectional contrastive learning to ensure high consistency between input and output images. Second, we propose a novel patch alignment loss to address the commonly observed misalignment issue in paired medical image datasets. Third...
首先,作者定义了CIC( (Increase of Confidence)的概念,既相对于baseline图片的置信度增量 对获取的特征图进行channel-wise遍历,对每层特征图进行上采样+归一化,与原始图片进行pixel-wise相乘融合,然后送进网络获取目标类别score(softmax后),减去baseline的目标类别score,获取CIC。再进行softmax操作来保证所有CIC之和为1...
SelfMatch: combining contrastive self-supervision and consistency for semi-supervised learning. arXiv preprint arXiv:2101.06480. Google Scholar Kong, Guerra, Pham, Mitchell, Lynch, Warrier, Ramsay, Heriot, 2019 J.C. Kong, G.R. Guerra, T. Pham, C. Mitchell, A.C. Lynch, S.K. Warrier, ...