Deep learning for medical image analysis requires large quantities of high-quality imaging data for training purposes, which could be often less available due to existence of heavy noise in particular imaging modalities. This issue is especially obvious in cerebral microbleed (CMB) detection, since CMBs are more discernable on long echo time (TE) susceptibility weighted imaging (SWI) data, which are unfortunately much noisier than those with shorter TE. In this paper we present an effective unsupervised image denoising scheme with application to boosting the performance of deep learning based CMB detection. The proposed content-adaptive denoising technique uses the log-determinant of covariance matrices formed by highly correlated image contents retrieved from the input itself to implicitly and efficiently exploit sparsity in PCA domain. The numerical solution to the corresponding optimization problem comes down to an adaptive squeeze-and-shrink (ASAS) operation on the underlying PCA coefficients. Obviously, the ASAS denoising does not rely on any external dataset and could be better fit the input image data. Experiments on medical image datasets with synthetic Gaussian white noise demonstrate that the proposed ASAS scheme is highly competitive among state-of-the-art sparsity based approaches as well as deep learning based method. When applied to the deep learning based CMB detection on the real-world TE3 SWI dataset, the proposed ASAS denoising could improve the precision by 18.03%, sensitivity by 7.64%, and increase the correlation between counts of ground truth and automated detection by 19.87%.