Aug 2026· Journal of imaging informatics in medicine· 0 citations· 10 references
Medicine
TL;DR
An automated, data quality-oriented preprocessing pipeline that rewrites radiology reports at the sentence level to reduce external references is developed and can serve as an effective preprocessing step for improving the suitability of chest X-ray report datasets for multimodal model development.
It is demonstrated that LLMs can be effectively employed to generate supervision labels for medical imaging tasks and that the proposed approach offers a scalable and low-cost solution for preliminary disease screening, particularly in healthcare environments with limited expert availability.
Qing-Yuan Zhang, Pardeep Vasudev, Kezhi Li et al.· Frontiers in Digital Health· 0 citations
A structured Retrieval-Augmented Generation (RAG) framework that leverages annotated mammography reports as in-context demonstrations, enabling joint named entity and relation extraction without task-specific fine-tuning is proposed and achieves performance comparable to fine-tuned BETO models in low-resource settings,...
Eduardo Godoy, J. De Ferrari, Sofia Lazo et al.· BMC Medical Informatics and...· 1 citation· ⚡1
Chest X-ray report generation systems are valuable for assisting disease diagnosis and improving healthcare efficiency. However, existing methods still face two key challenges. First, multiple diseases often co-occur, leading to a combinatorial explosion of label combinations and sparse supervision for learning a gener...
Hong-Ze Zhu, Hong Liu, Ya-Wen Huang et al.· IEEE Transactions on Medical...· 0 citations
Through hierarchical gated co-attention, this approach dynamically aligns image and text representations, addressing the limitations of static fusion and providing a foundation for interpretable, real-time retrieval systems that can accelerate and improve clinical decision support.
This work proposes HarMoE, a dataset-aware mixture-of-experts framework that learns shared cross-dataset medical semantics while confining source-specific variation to lightweight residual experts in deeper decoder layers, and trains in a unified disease vocabulary with masked multi-dataset supervision, enabling the mo...
Haozhe Luo, Zi-Yu Zhou, Shelley Zixin Shu et al.· 0 citations
This work introduces a token-aligned annotation scheme that is efficient, scalable, and naturally integrates with modern deep learning architectures like Transformers, and shows that models trained with this scheme achieved higher localization performance than weakly supervised baselines trained on image-level labels a...
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
