Papers by Liangqiong Qu
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning (2024.findings-emnlp)
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| Challenge: | Brain CT report generation is important to aid physicians in diagnosing cranial diseases. |
| Approach: | They propose a Pathological Clue-driven Representation Learning model to build cross-modal representations based on pathological clues and adapt them for text generation. |
| Outcome: | The proposed method outperforms previous methods and achieves SoTA performance. |
Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation (2023.emnlp-main)
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| Challenge: | Existing methods for automatic Brain CT reports are limited by coarse-grained supervision and coupled cross-modal alignment. |
| Approach: | They propose a pathological Graph-driven cross-modal alignment model that learns fine-grained visual cues and aligns them with textual words. |
| Outcome: | The proposed model can improve the automatic generation of Brain CT reports and contribute to improved cranial disease diagnosis. |