AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Multi-Label Chest X-Ray Image Classification With Single Positive Labels.

IEEE transactions on medical imaging Dec 2, 2024
Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduc...
Humans Deep Learning Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning Radiography, Thoracic Algorithms Databases, Factual Lung Thorax
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Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...
SARS-CoV-2 Lung Image Processing, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning COVID-19 Algorithms Databases, Factual Tomography, X-Ray Computed Humans Neural Networks, Computer
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External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.

Japanese journal of radiology Nov 30, 2024
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Radiation Dosage Female Aged Early Detection of Cancer Aged, 80 and over Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Japan Humans Deep Learning Retrospective Studies Lung Neoplasms Male Tomography, X-Ray Computed Multiple Pulmonary Nodules Middle Aged Solitary Pulmonary Nodule Lung
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BMSMM-Net: A Bone Metastasis Segmentation Framework Based on Mamba and Multiperspective Extraction.

Academic radiology Nov 30, 2024
RATIONALE AND OBJECTIVES: Metastatic bone tumors significantly reduce patients' quality of life and expedite cancer spread. Traditional diagnostic methods rely on time-consuming manual annotations by radiologists, which are prone to subjectivity. Emp...
Bone Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Deep Learning
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Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis.

European radiology Nov 29, 2024
PURPOSE: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data.
Female Risk Factors Aged Adult Humans Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Male Radiation Dosage Middle Aged Propensity Score Deep Learning Neoplasms, Radiation-Induced
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CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.

Medical & biological engineering & computing Nov 29, 2024
Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis. However, most previous studies neither effectively learn label correlations nor take full advantage...
Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Humans Algorithms Deep Learning
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Multi-scale region selection network in deep features for full-field mammogram classification.

Medical image analysis Nov 26, 2024
Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer...
Algorithms Female Diagnosis, Computer-Assisted Humans Breast Neoplasms Deep Learning Mammography Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted
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Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.

Academic radiology Nov 25, 2024
RATIONALE AND OBJECTIVES: Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detecti...
Humans Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Solitary Pulmonary Nodule Radiologists Radiographic Image Interpretation, Computer-Assisted Observer Variation Female Sensitivity and Specificity Radiography, Thoracic Reproducibility of Results Male Aged Middle Aged Lung Neoplasms
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Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.

Clinical neuroradiology Nov 25, 2024
PURPOSE: Deep learning (DL) methods for detecting large vessel occlusion (LVO) in acute ischemic stroke (AIS) show promise, but the effect of computed tomography angiography (CTA) image quality on DL performance is unclear. Our study investigates the...
Radiographic Image Interpretation, Computer-Assisted Multidetector Computed Tomography Computed Tomography Angiography Cerebral Angiography Male Middle Aged Deep Learning Ischemic Stroke Aged Aged, 80 and over Sensitivity and Specificity Humans Female
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Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.

Scientific reports Nov 22, 2024
Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable ...
Radiographic Image Interpretation, Computer-Assisted Lung Diseases Radiography, Thoracic Lung Deep Learning Pneumoconiosis Humans COVID-19 SARS-CoV-2
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