标题： 主动提示调优使Gpt-40能够高效分类显微图像 显示英文标题

标题： Active Prompt Tuning Enables Gpt-40 To Do Efficient Classification Of Microscopy Images

摘要： 基于传统深度学习的显微镜图像中细胞特征分类方法需要耗费大量时间和人力来训练模型。目前的主要限制包括领域专家需要投入大量时间准备准确的标注数据，以及需要大量的输入图像数据。我们之前提出了一种解决方案，利用 OpenAI 的 GPT-4(V) 模型克服了这些挑战，在一个试点数据集上（来自 11 只小鼠大脑的 Iba-1 免疫染色组织切片）取得了成果。与使用传统的卷积神经网络（CNN）方法相比，试点数据集上的结果在准确性上相当，并且在吞吐效率上有显著提升。 本研究在此框架的基础上，使用了一个第二组独特且规模更大的显微镜图像数据集。我们的当前方法采用了更新且更快的模型 GPT-4o，同时优化了提示语设计。该方法在一个低倍率（10x）放大下捕获的显微镜图像数据集上进行了评估，这些图像是通过对总共 18 只小鼠（9 只 Lurcher 小鼠，9 只野生型对照）的小脑冠状切片用甲酚紫染色后获得的。我们使用该方法将这些图像分类为对照组或 Lurcher 突变体。在提示集中使用 6 只小鼠时，对 12 只小鼠中的 11 只（92%）实现了正确分类，相较于基线方法（CNN 模型快照集成），具有 96% 更高的效率，减少了图像需求，并降低了领域专家的时间和精力负担。这些结果证实了我们的方法在多个来自不同脑区和放大倍率的数据集上均有效，且开销极小。 显示英文摘要

摘要： Traditional deep learning-based methods for classifying cellular features in microscopy images require time- and labor-intensive processes for training models. Among the current limitations are major time commitments from domain experts for accurate ground truth preparation; and the need for a large amount of input image data. We previously proposed a solution that overcomes these challenges using OpenAI's GPT-4(V) model on a pilot dataset (Iba-1 immuno-stained tissue sections from 11 mouse brains). Results on the pilot dataset were equivalent in accuracy and with a substantial improvement in throughput efficiency compared to the baseline using a traditional Convolutional Neural Net (CNN)-based approach. The present study builds upon this framework using a second unique and substantially larger dataset of microscopy images. Our current approach uses a newer and faster model, GPT-4o, along with improved prompts. It was evaluated on a microscopy image dataset captured at low (10x) magnification from cresyl-violet-stained sections through the cerebellum of a total of 18 mouse brains (9 Lurcher mice, 9 wild-type controls). We used our approach to classify these images either as a control group or Lurcher mutant. Using 6 mice in the prompt set the results were correct classification for 11 out of the 12 mice (92%) with 96% higher efficiency, reduced image requirements, and lower demands on time and effort of domain experts compared to the baseline method (snapshot ensemble of CNN models). These results confirm that our approach is effective across multiple datasets from different brain regions and magnifications, with minimal overhead.