ArticleJournal of pathology informatics2023
Transformer-based personalized attention mechanism for medical images with clinical records.
Article in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Review
- Explaining alerts from a pediatric risk prediction model using clinical text.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma.Journal of pathology informatics · 2024Article
- 3D residual attention hierarchical fusion for real-time detection of the prostate capsule.BMC medical imaging · 2024Article
- A survey of Transformer applications for histopathological image analysis: New developments and future directions.Biomedical engineering online · 2023Review
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based on both the images and various clinical records. Consequently, pathologists examine medical images with prior knowledge of the patients and the attention regions may change depending on the clinical records. In this study, we propose a method, called the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.