关注纹理：重新思考用于医学图像分类的掩码自编码器中的预训练
Focus on Texture: Rethinking Pre-training in Masked Autoencoders for Medical Image Classification
AI总结：
针对医学图像分类中纹理线索重要但MSE预训练导致模糊的问题，提出GLCM-MAE框架，利用基于灰度共生矩阵的可微损失保留形态特征，在多项医学图像分类任务上超越现有方法。
摘要：
掩码自编码器（MAEs）已成为自然图像自监督表示学习的主导策略，其中模型通过最小化原始与重建RGB值之间的逐像素均方误差（MSE）作为损失来进行预训练。我们观察到，MSE倾向于生成模糊的图像重建，但在自然图像中仍有效，因为它保留了主要边缘。然而，在医学成像中，当纹理线索对于视觉异常的分类更为重要时，该策略就会失效。受影像组学研究中灰度共生矩阵（GLCM）特征的启发，我们提出了一种基于MAE的新型预训练框架GLCM-MAE，使用基于匹配GLCM的重建损失。GLCM捕获图像中的强度和空间关系，因此所提出的损失有助于保留形态特征。此外，我们提出了一种新的公式，将匹配GLCM矩阵转化为可微的损失函数。我们证明，使用所提出的GLCM损失对医学图像进行无监督预训练可改善下游任务的表示。GLCM-MAE在四项任务上超越了当前最先进水平：超声图像胆囊癌检测提升2.1%，超声乳腺癌检测提升3.1%，X射线肺炎检测提升0.5%，CT COVID检测提升0.6%。源代码和预训练模型可在 https://github.com/ChetanMadan/GLCM-MAE 获取。
英文摘要：
Masked Autoencoders (MAEs) have emerged as a dominant strategy for self-supervised representation learning in natural images, where models are pre-trained to reconstruct masked patches with a pixel-wise mean squared error (MSE) between original and reconstructed RGB values as the loss. We observe that MSE encourages blurred image re-construction, but still works for natural images as it preserves dominant edges. However, in medical imaging, when the texture cues are more important for classification of a visual abnormality, the strategy fails. Taking inspiration from Gray Level Co-occurrence Matrix (GLCM) feature in Radiomics studies, we propose a novel MAE based pre-training framework, GLCM-MAE, using reconstruction loss based on matching GLCM. GLCM captures intensity and spatial relationships in an image, hence proposed loss helps preserve morphological features. Further, we propose a novel formulation to convert matching GLCM matrices into a differentiable loss function. We demonstrate that unsupervised pre-training on medical images with the proposed GLCM loss improves representations for downstream tasks. GLCM-MAE outperforms the current state-of-the-art across four tasks - gallbladder cancer detection from ultrasound images by 2.1%, breast cancer detection from ultrasound by 3.1%, pneumonia detection from x-rays by 0.5%, and COVID detection from CT by 0.6%. Source code and pre-trained models are available at: https://github.com/ChetanMadan/GLCM-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。
