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Radiographic Image Interpretation, Computer-Assisted

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Efficient Lung Segmentation from Chest Radiographs using Transfer Learning and Lightweight Deep Architecture.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lung delineation constitutes a critical preprocessing stage for X-ray-based diagnosis and follow-up. However, automatic lung segmentation from chest radiographs (CXR) poses a challenging problem due to anatomical structures' varying shapes and sizes,...

Deep learning based coronary vessels segmentation in X-ray angiography using temporal information.

Medical image analysis
Invasive coronary angiography (ICA) is the gold standard imaging modality during cardiac interventions. Accurate segmentation of coronary vessels in ICA is required for aiding diagnosis and creating treatment plans. Current automated algorithms for v...

Histological proven AI performance in the UKLS CT lung cancer screening study: Potential for workload reduction.

European journal of cancer (Oxford, England : 1990)
PURPOSE: Artificial intelligence (AI) could reduce lung cancer screening computer tomography (CT)-reading workload if used as a first-reader, ruling-out negative CT-scans at baseline. Evidence is lacking to support AI performance when compared to gol...

Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.

BMC gastroenterology
OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligenc...

Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence.

Journal of the American College of Radiology : JACR
OBJECTIVE: Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as t...

Learning Consistent Semantic Representation for Chest X-ray via Anatomical Localization in Self-Supervised Pre-Training.

IEEE journal of biomedical and health informatics
Despite the similar global structures in Chest X-ray (CXR) images, the same anatomy exhibits varying appearances across images, including differences in local textures, shapes, colors, etc. Learning consistent representations for anatomical semantics...

OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN.

IEEE journal of biomedical and health informatics
The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-pre...

Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BMC medical imaging
BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonar...

Two-Stage Deep Learning Model for Adrenal Nodule Detection on CT Images: A Retrospective Study.

Radiology
Background The detection and classification of adrenal nodules are crucial for their management. Purpose To develop and test a deep learning model to automatically depict adrenal nodules on abdominal CT images and to simulate triaging performance in ...

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation.

IEEE transactions on medical imaging
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the ...