Position-Aware Anti-Aliasing Filters for 3D Medical Image Analysis

Abstract

Maximum pooling, average pooling, and strided convolution are three widely adopted down-sampling approaches in deep learning based 3D medical image analysis. However, these methods have their own pros and cons. Maximum pooling and strided convolution are advantageous in capturing the discriminative features but often lead to the aliasing problem. In comparison, average pooling anti-aliases the representations but produces less discriminative representations. To address such shortcoming, anti-aliased maximum pooling (MaxBlurPool) uses low-pass filters to mitigate the aliasing effect. However, these filters are designed to be fixed, making it difficult to adapt to various spatial positions. In this paper, we propose position-aware anti-aliasing filters (PASS) to learn spatially adaptive low-pass filters. Compared to maximum pooling, PASS integrates a one-layer local attention module, whose computational cost is minimal. Thus, PASS can be incorporated into existing network architecture with minor efforts. In comparison to previous anti-aliased counterparts, PASS brings consistent and clear performance gains on brain tumor segmentation, pulmonary nodule detection, and cerebral hemorrhage detection. Besides, PASS also greatly improves the model robustness under adversarial attack.