  Weak supervision learning on classification labels has demonstrated high
performance in various tasks, while a few pixel-level fine annotations are also
affordable. Naturally a question comes to us that whether the combination of
pixel-level (e.g., segmentation) and image level (e.g., classification)
annotation can introduce further improvement. However in computational
pathology this is a difficult task for this reason: High resolution of whole
slide images makes it difficult to do end-to-end classification model training,
which is challenging to research of weak or hybrid supervision learning in the
past. To handle this problem, we propose a hybrid supervision learning
framework for this kind of high resolution images with sufficient image-level
coarse annotations and a few pixel-level fine labels. This framework, when
applied in training patch model, can carefully make use of coarse image-level
labels to refine generated pixel-level pseudo labels. Complete strategy is
proposed to suppress pixel-level false positives and false negatives. A large
hybrid annotated dataset is used to evaluate the effectiveness of hybrid
supervision learning. By extracting pixel-level pseudo labels in initially
image-level labeled samples, we achieve 5.2% higher specificity than purely
training on existing labels while retaining 100% sensitivity, in the task of
image-level classification to be positive or negative.
