Rotation-Scale Equivariant Steerable Filters
This addresses the challenge of analyzing digital histology images captured at arbitrary orientations and magnifications, which is crucial for medical diagnosis, though it is an incremental improvement over existing equivariant methods.
The paper tackles the problem of limited generalization in histopathology image analysis by proposing Rotation-Scale Equivariant Steerable Filters (RSESF) to jointly incorporate rotation and scale equivariance into CNNs, resulting in outperforming other approaches on gland segmentation datasets with fewer parameters and GPU resources.
Incorporating either rotation equivariance or scale equivariance into CNNs has proved to be effective in improving models' generalization performance. However, jointly integrating rotation and scale equivariance into CNNs has not been widely explored. Digital histology imaging of biopsy tissue can be captured at arbitrary orientation and magnification and stored at different resolutions, resulting in cells appearing in different scales. When conventional CNNs are applied to histopathology image analysis, the generalization performance of models is limited because 1) a part of the parameters of filters are trained to fit rotation transformation, thus decreasing the capability of learning other discriminative features; 2) fixed-size filters trained on images at a given scale fail to generalize to those at different scales. To deal with these issues, we propose the Rotation-Scale Equivariant Steerable Filter (RSESF), which incorporates steerable filters and scale-space theory. The RSESF contains copies of filters that are linear combinations of Gaussian filters, whose direction is controlled by directional derivatives and whose scale parameters are trainable but constrained to span disjoint scales in successive layers of the network. Extensive experiments on two gland segmentation datasets demonstrate that our method outperforms other approaches, with much fewer trainable parameters and fewer GPU resources required. The source code is available at: https://github.com/ynulonger/RSESF.