Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
Recent years have experienced phenomenal growth in computer-aided diagnosis systems
based on machine learning algorithms for anomaly detection tasks in the medical image …
based on machine learning algorithms for anomaly detection tasks in the medical image …
[HTML][HTML] Lightweight multi-scale classification of chest radiographs via size-specific batch normalization
Abstract Background and Objective: Convolutional neural networks are widely used to
detect radiological findings in chest radiographs. Standard architectures are optimized for …
detect radiological findings in chest radiographs. Standard architectures are optimized for …
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
Deep learning, in recent times, has made remarkable strides when it comes to impressive
performance for many tasks, including medical image processing. One of the contributing …
performance for many tasks, including medical image processing. One of the contributing …
[HTML][HTML] Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings
ML Kromrey, L Steiner, F Schön, J Gamain, C Roller… - Healthcare, 2024 - mdpi.com
Background: The integration of artificial intelligence (AI) into radiology aims to improve
diagnostic accuracy and efficiency, particularly in settings with limited access to expert …
diagnostic accuracy and efficiency, particularly in settings with limited access to expert …
Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations
Annotation errors are a challenge not only during training of machine learning models, but
also during their evaluation. Label variations and inaccuracies in datasets often manifest as …
also during their evaluation. Label variations and inaccuracies in datasets often manifest as …
Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma
Purpose Radiomics-based machine learning (ML) models of amino acid positron emission
tomography (PET) images have shown efficiency in glioma prediction tasks. However, their …
tomography (PET) images have shown efficiency in glioma prediction tasks. However, their …
Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs
H Bettinger, G Lenczner, J Guigui, L Rotenberg… - Diagnostics, 2024 - mdpi.com
The purpose of the study was to assess the performance of readers in diagnosing thoracic
anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based …
anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based …
Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation
C Lee, S Park, CI Shin, WH Choi, HJ Park… - arxiv preprint arxiv …, 2024 - arxiv.org
Recent medical vision-language models (VLMs) have shown promise in 2D medical image
interpretation. However extending them to 3D medical imaging has been challenging due to …
interpretation. However extending them to 3D medical imaging has been challenging due to …
[HTML][HTML] Explainable artificial intelligence in deep learning–based detection of aortic elongation on chest X-ray images
Aims Aortic elongation can result from age-related changes, congenital factors, aneurysms,
or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases …
or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases …
Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation
We conducted a prospective study to measure the clinical impact of an explainable machine
learning system on interobserver agreement in chest radiograph interpretation. The AI …
learning system on interobserver agreement in chest radiograph interpretation. The AI …