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Found 6 papers, 4 papers with code
Automatic recognition of abdominal lymph nodes from clinical text
We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Additionally, using these improved annotations in classification supervision, we demonstrate substantial advancements in model quality, with an increase of 1. 7 pp in AUROC over models trained with annotations from the state-of-the-art approach.
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
Chest radiographs are one of the most common diagnostic modalities in clinical routine.
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction.
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure.
Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database
Obtaining semantic labels on a large scale radiology image database (215, 786 key images from 61, 845 unique patients) is a prerequisite yet bottleneck to train highly effective deep convolutional neural network (CNN) models for image recognition.
