SLDPC: Slide-Level Dual-Prompt Collaboration for few-shot whole slide image classification

Abstract

Digital pathology standardizes diagnostic workflows through the digitization of conventional slides and the integration of algorithmic analysis. Few-shot Weakly Supervised Whole Slide Image (WSI) Classification (FSWC) represents a critical challenge in digital pathology. Conventional Multiple Instance Learning (MIL) methods rely on large volumes of annotated data and are susceptible to distribution shifts. Vision-Language Model (VLM)-based prompt learning methods enable parameter-efficient few-shot learning but are limited to patch-level feature aggregation, failing to model slide-level diagnostic information. As slide-level information is crucial for understanding tissue architecture and lesion distribution, we propose a Slide-Level Dual-Prompt Collaboration (SLDPC) framework for the FSWC task. Specifically, SLDPC leverages the representation learning capability of a slide-level VLM to perform prompt tuning directly at the slide level. A base prompt $P$ is first obtained through continuous prompt initialization training and subsequently cloned to derive a parallel prompt $P^{\prime }$. In addition, bidirectional InfoNCE loss is employed to enhance feature-level alignment. During inference, a weighted fusion mechanism is introduced to combine both prompts and achieve efficient adaptation of slide-level multimodal representations. Experimental evaluation on four datasets validates the superiority of SLDPC. The results demonstrate that slide-level prompt learning effectively addresses FSWC challenges and improves model performance.