利用预训练视觉模型和双文本解码器自动描述肺穿刺图像的细胞学发现：初步研究
Automated Description Generation of Cytologic Findings for Lung Cytological Images Using a Pretrained Vision Model and Dual Text Decoders: Preliminary Study
AI总结：
本文提出利用预训练视觉模型和双文本解码器自动生成肺穿刺图像的细胞学发现，通过CNN分类和Transformer生成文本，实现高准确率的细胞学报告生成。
摘要：
目的：细胞学在肺癌诊断中起关键作用。肺细胞学涉及对标本中的细胞形态进行表征并报告相应结果，这些任务非常耗时。本研究提出了一种技术，通过细胞学图像生成细胞学发现，以帮助肺细胞学报告。方法：本研究从206名患者收集的细胞学标本中检索了801个图像块，将结果分配给每个图像作为生成细胞学发现的数据集。所提出的方法包括一个视觉模型和双文本解码器。在前者中，使用卷积神经网络（CNN）将给定图像分类为良性或恶性，并从中间层提取与图像相关的特征。为良性和恶性细胞分别准备独立的文本解码器，根据CNN分类结果切换文本解码器。文本解码器使用从CNN获得的特征生成文本。结果：自动良性与恶性病例分类的灵敏度和特异度分别为100%和96.4%，显著性图指示了特征良性与恶性区域。生成文本的语法和风格已被确认正确，BLEU-4得分为0.828，比现有基于LLM的图像-描述方法和单文本解码器消融模型表现更好。结论：实验结果表明，所提出的方法对肺细胞学分类和生成细胞学发现是有用的。
英文摘要：
Objective: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the specimen and reporting the corresponding findings, which are extremely burdensome tasks. In this study, we propose a technique to generate cytologic findings from for cytologic images to assist in the reporting of pulmonary cytology. Methods: For this study, 801 patch images were retrieved using cytology specimens collected from 206 patients; the findings were assigned to each image as a dataset for generating cytologic findings. The proposed method consists of a vision model and dual text decoders. In the former, a convolutional neural network (CNN) is used to classify a given image as benign or malignant, and the features related to the image are extracted from the intermediate layer. Independent text decoders for benign and malignant cells are prepared for text generation, and the text decoder switches according to the CNN classification results. The text decoder is configured using a Transformer that uses the features obtained from the CNN for generating findings. Results: The sensitivity and specificity were 100% and 96.4%, respectively, for automated benign and malignant case classification, and the saliency map indicated characteristic benign and malignant areas. The grammar and style of the generated texts were confirmed correct, achieving a BLEU-4 score of 0.828, reflecting high degree of agreement with the gold standard, outperforming existing LLM-based image-captioning methods and single-text-decoder ablation model. Conclusion: Experimental results indicate that the proposed method is useful for pulmonary cytology classification and generation of cytologic findings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
