  Weakly supervised learning has been rapidly advanced in biomedical image
analysis to achieve pixel-wise labels (segmentation) from image-wise
annotations (classification), as biomedical images naturally contain image-wise
labels in many scenarios. The current weakly supervised learning algorithms
from the computer vision community are largely designed for focal objects
(e.g., dogs and cats). However, such algorithms are not optimized for diffuse
patterns in biomedical imaging (e.g., stains and fluorescence in microscopy
imaging). In this paper, we propose a novel class-aware codebook learning
(CaCL) algorithm to perform weakly supervised learning for diffuse image
patterns. Specifically, the CaCL algorithm is deployed to segment protein
expressed brush border regions from histological images of human duodenum. Our
contribution is three-fold: (1) we approach the weakly supervised segmentation
from a novel codebook learning perspective; (2) the CaCL algorithm segments
diffuse image patterns rather than focal objects; and (3) the proposed
algorithm is implemented in a multi-task framework based on Vector
Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction,
classification, feature embedding, and segmentation. The experimental results
show that our method achieved superior performance compared with baseline
weakly supervised algorithms. The code is available at
https://github.com/ddrrnn123/CaCL.
