Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation

Yanzhao Shi, Junzhong Ji, Xiaodan Zhang, Liangqiong Qu, Ying Liu


Abstract
The automatic Brain CT reports generation can improve the efficiency and accuracy of diagnosing cranial diseases. However, current methods are limited by 1) coarse-grained supervision: the training data in image-text format lacks detailed supervision for recognizing subtle abnormalities, and 2) coupled cross-modal alignment: visual-textual alignment may be inevitably coupled in a coarse-grained manner, resulting in tangled feature representation for report generation. In this paper, we propose a novel Pathological Graph-driven Cross-modal Alignment (PGCA) model for accurate and robust Brain CT report generation. Our approach effectively decouples the cross-modal alignment by constructing a Pathological Graph to learn fine-grained visual cues and align them with textual words. This graph comprises heterogeneous nodes representing essential pathological attributes (i.e., tissue and lesion) connected by intra- and inter-attribute edges with prior domain knowledge. Through carefully designed graph embedding and updating modules, our model refines the visual features of subtle tissues and lesions and aligns them with textual words using contrastive learning. Extensive experimental results confirm the viability of our method. We believe that our PGCA model holds the potential to greatly enhance the automatic generation of Brain CT reports and ultimately contribute to improved cranial disease diagnosis.
Anthology ID:
2023.emnlp-main.408
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6617–6630
Language:
URL:
https://aclanthology.org/2023.emnlp-main.408
DOI:
10.18653/v1/2023.emnlp-main.408
Bibkey:
Cite (ACL):
Yanzhao Shi, Junzhong Ji, Xiaodan Zhang, Liangqiong Qu, and Ying Liu. 2023. Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6617–6630, Singapore. Association for Computational Linguistics.
Cite (Informal):
Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation (Shi et al., EMNLP 2023)
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PDF:
https://aclanthology.org/2023.emnlp-main.408.pdf
Video:
 https://aclanthology.org/2023.emnlp-main.408.mp4