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Yanzhao Shi

4 accepted papers

2026

MoEA-Net: Modality-Incremental Expert Aggregation Network for Retinal Prognostic Prediction

AAAI 2026technical

Automated analysis of temporal changes in multimodal retinal images is critical for the prognostic assessment of ophthalmic diseases. Unlike traditional single-timepoint diagnosis, tracking longitudinal changes across multiple imaging modalities introduces significant data bias challenges: (1) Imbal

Cited by 0SourcePDFScholar
2025

MEPNet: Medical Entity-Balanced Prompting Network for Brain CT Report Generation

AAAI 2025technical

The automatic generation of brain CT reports has gained widespread attention, given its potential to assist radiologists in diagnosing cranial diseases. However, brain CT scans involve extensive medical entities, such as diverse anatomy regions and lesions, exhibiting highly inconsistent spatial pat…

2024

See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning

EMNLP 2024finding

Brain CT report generation is significant to aid physicians in diagnosing cranial diseases.Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report.However, there exist some challenges: 1) Redundant visual representing…

2023

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

EMNLP 2023long main

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) coup…

Cited by 0SourceScholar