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.
Recent vision-language models for chest X-ray understanding are largely built on image-report alignment and therefore rely heavily on MIMIC-CXR as the dominant pretraining source. While effective at scale, this paradigm underexplores an important alternative source of supervision: a range of existing multi-label classi...
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...
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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