Zero-shot Nuclei Detection via Visual-Language Pre-trained Models

• URL: http://arxiv.org/abs/2306.17659v1
• Date: Fri, 30 Jun 2023 13:44:13 GMT
• Title: Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
• Authors: Yongjian Wu, Yang Zhou, Jiya Saiyin, Bingzheng Wei, Maode Lai, Jianzhong Shou, Yubo Fan, Yan Xu
• Abstract summary: Large-scale visual-language pre-trained models have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection on H&E images via VLPMs remains underexplored. In this paper, we attempt to explore the potential of the object-level VLPM, Grounded Language-Image Pre-training model, for zero-shot nuclei detection.
• Score: 19.526504045149895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale visual-language pre-trained models (VLPM) have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection on H\&E images via VLPMs remains underexplored. The large gap between medical images and the web-originated text-image pairs used for pre-training makes it a challenging task. In this paper, we attempt to explore the potential of the object-level VLPM, Grounded Language-Image Pre-training (GLIP) model, for zero-shot nuclei detection. Concretely, an automatic prompts design pipeline is devised based on the association binding trait of VLPM and the image-to-text VLPM BLIP, avoiding empirical manual prompts engineering. We further establish a self-training framework, using the automatically designed prompts to generate the preliminary results as pseudo labels from GLIP and refine the predicted boxes in an iterative manner. Our method achieves a remarkable performance for label-free nuclei detection, surpassing other comparison methods. Foremost, our work demonstrates that the VLPM pre-trained on natural image-text pairs exhibits astonishing potential for downstream tasks in the medical field as well. Code will be released at https://github.com/wuyongjianCODE/VLPMNuD.

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