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Found 8 papers, 1 papers with code
Knowledge-grounded Adaptation Strategy for Vision-language Models: Building Unique Case-set for Screening Mammograms for Residents Training
A visual-language model (VLM) pre-trained on natural images and text pairs poses a significant barrier when applied to medical contexts due to domain shift.
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions.
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.
Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs
The results show the internal performance of each of the 5 pathologies outperformed external performance on both of the models.
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability.
Learning Semantics for Image Annotation
Image search and retrieval engines rely heavily on textual annotation in order to match word queries to a set of candidate images.
Image Annotation using Multi-Layer Sparse Coding
Vocabulary words are the fine labels to be associated with images.
Feature-Independent Context Estimation for Automatic Image Annotation
Automatic image annotation is a highly valuable tool for image search, retrieval and archival systems.
