PathoTune: Adapting Visual Foundation Model to Pathological Specialists
<|reference_start|>PathoTune: Adapting Visual Foundation Model to Pathological Specialists: As natural image understanding moves towards the pretrain-finetune era, research in pathology imaging is concurrently evolving. Despite the predominant focus on pretraining pathological foundation models, how to adapt foundation models to downstream tasks is little explored. For downstream adaptation, we propose the existence of two domain gaps, i.e., the Foundation-Task Gap and the Task-Instance Gap. To mitigate these gaps, we introduce PathoTune, a framework designed to efficiently adapt pathological or even visual foundation models to pathology-specific tasks via multi-modal prompt tuning. The proposed framework leverages Task-specific Visual Prompts and Task-specific Textual Prompts to identify task-relevant features, along with Instance-specific Visual Prompts for encoding single pathological image features. Results across multiple datasets at both patch-level and WSI-level demonstrate its superior performance over single-modality prompt tuning approaches. Significantly, PathoTune facilitates the direct adaptation of natural visual foundation models to pathological tasks, drastically outperforming pathological foundation models with simple linear probing. The code is available at https://github.com/openmedlab/PathoDuet.<|reference_end|>
@article{lu2024pathotune:,
    title={PathoTune: Adapting Visual Foundation Model to Pathological Specialists},
    author={Jiaxuan Lu, Fang Yan, Xiaofan Zhang, Yue Gao, Shaoting Zhang},
    journal={arXiv preprint arXiv:2403.16497},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16497},
    primaryClass={cs.CV cs.LG}
}