Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy of MAEs is likely to result in areas of lesions being overlooked by the model. At the same time, inconsistencies between the pre-training and fine-tuning phases impede the performance and efficiency of MAE in medical image classification. To address these issues, we propose a medical supervised masked autoencoder (MSMAE) in this paper. In the pre-training phase, MSMAE precisely masks medical images via the attention maps obtained from supervised training, contributing to the representation learning of human tissue in the lesion area. During the fine-tuning phase, MSMAE is also driven by attention to the accurate masking of medical images. This improves the computational efficiency of the MSMAE while increasing the difficulty of fine-tuning, which indirectly improves the quality of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE achieves state-of-the-art performance in case with three official medical datasets for various diseases. Meanwhile, transfer learning for MSMAE also demonstrates the great potential of our approach for medical semantic segmentation tasks. Moreover, the MSMAE accelerates the inference time in the fine-tuning phase by 11.2% and reduces the number of floating-point operations (FLOPs) by 74.08% compared to a traditional MAE.

翻译：掩码自编码器（MAEs）近年来在医学图像的分类与语义分割中展现出显著潜力。由于人体组织的高度相似性，医学图像中即使微小的变化也可能代表病变组织，因此需要精细化的检测来定位病变区域。MAE的随机掩码策略可能导致模型忽略病灶区域。同时，预训练与微调阶段之间的不一致性阻碍了MAE在医学图像分类中的性能与效率。为解决这些问题，本文提出一种医学监督掩码自编码器（MSMAE）。在预训练阶段，MSMAE通过监督训练获取的注意力图对医学图像进行精确掩码，从而促进病变区域人体组织的表征学习。在微调阶段，MSMAE同样借助注意力机制驱动医学图像的精确掩码。这不仅提升了MSMAE的计算效率，同时增加了微调难度，间接提高了MSMAE医学诊断的质量。大量实验表明，在三个官方医学数据集针对不同疾病的案例中，MSMAE均达到了最先进的性能。此外，MSMAE的迁移学习能力也证明了本方法在医学语义分割任务中的巨大潜力。与传统MAE相比，MSMAE在微调阶段将推理时间加速了11.2%，并将浮点运算次数（FLOPs）减少了74.08%。