医学监督掩码自编码器：为医学图像分类设计更好的掩码策略与高效微调方案
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification
AI总结：
提出医学监督掩码自编码器（MSMAE），利用监督注意力图进行精确掩码，解决MAE在医学图像分类中病变遗漏和预训练-微调不一致问题，实现最先进性能并显著提升效率。
摘要：
掩码自编码器（MAEs）在过去一年中在医学图像的分类和语义分割方面展现了巨大的潜力。由于人体组织的高度相似性，医学图像中即使是微小的变化也可能代表病变组织，因此需要进行细粒度检查以精确定位病变组织。MAEs的随机掩码策略很可能导致病变区域被模型忽略。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和效率。为了解决这些问题，本文提出了一种医学监督掩码自编码器（MSMAE）。在预训练阶段，MSMAE通过监督训练获得的注意力图精确掩码医学图像，有助于病变区域人体组织的表示学习。在微调阶段，MSMAE同样受注意力驱动，对医学图像进行精确掩码。这提高了MSMAE的计算效率，同时增加了微调的难度，间接提升了MSMAE医学诊断的质量。大量实验表明，在三个官方医学数据集上针对多种疾病，MSMAE取得了最先进的性能。同时，MSMAE的迁移学习也展示了我们的方法在医学语义分割任务中的巨大潜力。此外，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%。
英文摘要：
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.
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。
