Ensemble of Foundation Models for Pap Smear Cell Analysis

Pap smear cell classification represents a complex diagnostic challenge fundamentally distinct from natural image analysis. This clinical task demands dual capabilities: precise morphological characterization at cellular resolution and robust suppression of non-target tissue interference. Although recent pathological foundation models have created new paradigms for medical image analysis, performance gaps remain significant when applying histopathology-pretrained models to cervical cytopathology tasks. In this paper, we detail the cjdbehumble team's winning solution for the Pap Smear Cell Classification Challenge (PS3C). Our approach addresses these challenges through two key innovations: First, we employ parameter-efficient adaptation of multiple foundation models using low-rank adaptation (LoRA). Then, we implement an ensemble learning strategy that integrates differential features from multiple foundational models through a unified decision-making mechanism. Our method achieved groundbreaking results in the PS3C, securing first place with outstanding performance metrics. The code is available at https://github.com/peter-fei/ISBI-PS3C.