One of the primary challenges in the development of Chest X-Ray (CXR) interpretation models has been the lack of large datasets with multilabel image annotations extracted from radiology reports. This paper proposes a CXR labeler that can simultaneously extracts fourteen observations from free-text ...
Standardized, automated labeling method, based on similarity to a previously validated five-label chest X-ray (CXR) detection explainable AI (xAI) model, using an xAI model-derived-atlas based approach. a Our quantitative model-derived atlas-based explainable AI system calculates a probability-of-...
2.1 Labeling Disease Names by Text Mining Overall, our approach produces labels using the reports in two passes. In the first iteration, we detected all the dis- ease concept in the corpus. The main body of each chest X-ray report is generally structured as "Comparis...
In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significa
(referred to as standalone testing) on a dataset of 20,000 chest X-ray cases from 12 healthcare centers in the U.S, which was independent from the dataset used to train the AI system. We also tested the generalizability of our findings on a publicly available chest X-ray dataset. Next...
Paper tables with annotated results for Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
In this work, we propose a graph-based deep semi-supervised framework for classifying COVID-19 Chext X-ray images based on an optimisation model for label diffusion. Through the minimisation of a normalised and non-smooth p=1 Dirichlet energy, the model generates meaningful pseudo-labels that ...
One particular cause for concern with NLP labels is the issue of systematic or structured mislabeling, where an abnormality is consistently labeled incorrectly in the same way. An example of this occurs in the ChestX-ray14 dataset where subcutaneous emphysema is frequently identified as (pulmonary)...
CXR, chest X-ray. Full size image Not confined to the metric itself, we observed an interesting finding that the model attention of the ViT model gets refined with increasing time T (Fig. 3b). As the AI model evolves with increasing time T, the self-attention of AI gets refined to ...
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the ear