Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Junjia Huang Haofeng Li Xiang Wan Guanbin Li
Multi-class cell nuclei detection is a fundamental prerequisite in the
diagnosis of histopathology. It is critical to efficiently locate and identify
cells with diverse morphology and distributions in digital pathological images.
Most existing methods take complex intermediate representations as learning
targets and rely on inflexible post-refinements while paying less attention to
various cell density and fields of view. In this paper, we propose a novel
Affine-Consistent Transformer (AC-Former), which directly yields a sequence of
nucleus positions and is trained collaboratively through two sub-networks, a
global and a local network. The local branch learns to infer distorted input
images of smaller scales while the global network outputs the large-scale
predictions as extra supervision signals. We further introduce an Adaptive
Affine Transformer (AAT) module, which can automatically learn the key spatial
transformations to warp original images for local network training. The AAT
module works by learning to capture the transformed image regions that are more
valuable for training the model. Experimental results demonstrate that the
proposed method significantly outperforms existing state-of-the-art algorithms
on various benchmarks.