Quantifying and leveraging classification uncertainty for chest radiograph assessment
The interpretation of chest radiographs is an essential task for the detection of thoracic
diseases and abnormalities. However, it is a challenging problem with high inter-rater …
diseases and abnormalities. However, it is a challenging problem with high inter-rater …
An accurate and explainable deep learning system improves interobserver agreement in the interpretation of chest radiograph
Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic
abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level …
abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level …
Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs
S Rajaraman, S Sornapudi, M Kohli… - 2019 41st Annual …, 2019 - ieeexplore.ieee.org
Respiratory diseases account for a significant proportion of deaths and disabilities across
the world. Chest X-ray (CXR) analysis remains a common diagnostic imaging modality for …
the world. Chest X-ray (CXR) analysis remains a common diagnostic imaging modality for …
[HTML][HTML] Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Background Chest radiograph interpretation is critical for the detection of thoracic diseases,
including tuberculosis and lung cancer, which affect millions of people worldwide each year …
including tuberculosis and lung cancer, which affect millions of people worldwide each year …
Quantifying and leveraging predictive uncertainty for medical image assessment
The interpretation of medical images is a challenging task, often complicated by the
presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest …
presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest …
Can we trust deep learning based diagnosis? the impact of domain shift in chest radiograph classification
While deep learning models become more widespread, their ability to handle unseen data
and generalize for any scenario is yet to be challenged. In medical imaging, there is a high …
and generalize for any scenario is yet to be challenged. In medical imaging, there is a high …
Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
A Majkowska, S Mittal, DF Steiner, JJ Reicher… - Radiology, 2020 - pubs.rsna.org
Background Deep learning has the potential to augment the use of chest radiography in
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …
clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty …
Handling label noise through model confidence and uncertainty: application to chest radiograph classification
E Calli, E Sogancioglu, ET Scholten… - Medical Imaging …, 2019 - spiedigitallibrary.org
In this work we analyze the effect of label noise in training and test data when performing
classification experiments on chest radiographs (CXRs) with modern deep learning …
classification experiments on chest radiographs (CXRs) with modern deep learning …
[HTML][HTML] Validation of a deep learning model for detecting chest pathologies from digital chest radiographs
Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to
errors. An automated system capable of categorizing chest radiographs based on the …
errors. An automated system capable of categorizing chest radiographs based on the …
Robust classification from noisy labels: Integrating additional knowledge for chest radiography abnormality assessment
Chest radiography is the most common radiographic examination performed in daily clinical
practice for the detection of various heart and lung abnormalities. The large amount of data …
practice for the detection of various heart and lung abnormalities. The large amount of data …