Automated Report Generation for Lung Cytological Images Using a CNN Vision Classifier and Multiple-Transformer Text Decoders: Preliminary Study

Abstract

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 report-generation technique for lung cytology images. In total, 71 benign and 135 malignant pulmonary cytology specimens were collected. Patch images were extracted from the captured specimen images, and the findings were assigned to each image as a dataset for report generation. The proposed method consists of a vision model and a text decoder. 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 report generation. Based on the evaluation 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 as correct and in better agreement with gold standard compared to existing LLM-based image-captioning methods and single-text-decoder ablation model. These results indicate that the proposed method is useful for pulmonary cytology classification and reporting.