PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models
<|reference_start|>PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models: The Vision Foundation Model has recently gained attention in medical image analysis. Its zero-shot learning capabilities accelerate AI deployment and enhance the generalizability of clinical applications. However, segmenting pathological images presents a special focus on the flexibility of segmentation targets. For instance, a single click on a Whole Slide Image (WSI) could signify a cell, a functional unit, or layers, adding layers of complexity to the segmentation tasks. Current models primarily predict potential outcomes but lack the flexibility needed for physician input. In this paper, we explore the potential of enhancing segmentation model flexibility by introducing various task prompts through a Large Language Model (LLM) alongside traditional task tokens. Our contribution is in four-fold: (1) we construct a computational-efficient pipeline that uses finetuned language prompts to guide flexible multi-class segmentation; (2) We compare segmentation performance with fixed prompts against free-text; (3) We design a multi-task kidney pathology segmentation dataset and the corresponding various free-text prompts; and (4) We evaluate our approach on the kidney pathology dataset, assessing its capacity to new cases during inference.<|reference_end|>
@article{cui2024pfps:,
    title={PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse
  Potential Outcomes Using Large Vision and Language Models},
    author={Can Cui, Ruining Deng, Junlin Guo, Quan Liu, Tianyuan Yao, Haichun
  Yang, Yuankai Huo},
    journal={arXiv preprint arXiv:2407.09979},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09979},
    primaryClass={cs.CV}
}