  This paper introduces the novel concept of few-shot weakly supervised
learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC.
A solution is proposed based on prompt learning and the utilization of a large
language model, GPT-4. Since a WSI is too large and needs to be divided into
patches for processing, WSI classification is commonly approached as a Multiple
Instance Learning (MIL) problem. In this context, each WSI is considered a bag,
and the obtained patches are treated as instances. The objective of FSWC is to
classify both bags and instances with only a limited number of labeled bags.
Unlike conventional few-shot learning problems, FSWC poses additional
challenges due to its weak bag labels within the MIL framework. Drawing
inspiration from the recent achievements of vision-language models (V-L models)
in downstream few-shot classification tasks, we propose a two-level prompt
learning MIL framework tailored for pathology, incorporating language prior
knowledge. Specifically, we leverage CLIP to extract instance features for each
patch, and introduce a prompt-guided pooling strategy to aggregate these
instance features into a bag feature. Subsequently, we employ a small number of
labeled bags to facilitate few-shot prompt learning based on the bag features.
Our approach incorporates the utilization of GPT-4 in a question-and-answer
mode to obtain language prior knowledge at both the instance and bag levels,
which are then integrated into the instance and bag level language prompts.
Additionally, a learnable component of the language prompts is trained using
the available few-shot labeled data. We conduct extensive experiments on three
real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer,
demonstrating the notable performance of the proposed method in bag and
instance classification. All codes will be available.
