Efficient Subclass Segmentation in Medical Images
Linrui Dai Wenhui Lei Xiaofan Zhang
As research interests in medical image analysis become increasingly
fine-grained, the cost for extensive annotation also rises. One feasible way to
reduce the cost is to annotate with coarse-grained superclass labels while
using limited fine-grained annotations as a complement. In this way,
fine-grained data learning is assisted by ample coarse annotations. Recent
studies in classification tasks have adopted this method to achieve
satisfactory results. However, there is a lack of research on efficient
learning of fine-grained subclasses in semantic segmentation tasks. In this
paper, we propose a novel approach that leverages the hierarchical structure of
categories to design network architecture. Meanwhile, a task-driven data
generation method is presented to make it easier for the network to recognize
different subclass categories. Specifically, we introduce a Prior Concatenation
module that enhances confidence in subclass segmentation by concatenating
predicted logits from the superclass classifier, a Separate Normalization
module that stretches the intra-class distance within the same superclass to
facilitate subclass segmentation, and a HierarchicalMix model that generates
high-quality pseudo labels for unlabeled samples by fusing only similar
superclass regions from labeled and unlabeled images. Our experiments on the
BraTS2021 and ACDC datasets demonstrate that our approach achieves comparable
accuracy to a model trained with full subclass annotations, with limited
subclass annotations and sufficient superclass annotations. Our approach offers
a promising solution for efficient fine-grained subclass segmentation in
medical images. Our code is publicly available here.