AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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A review of computer aided detection in mammography.

Clinical imaging
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography r...

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive redu...

Computer-aided detection in chest radiography based on artificial intelligence: a survey.

Biomedical engineering online
As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical...

Demystification of AI-driven medical image interpretation: past, present and future.

European radiology
The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship a...

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images.

IEEE transactions on medical imaging
Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The d...

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.

Medical image analysis
This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesion...

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

Academic radiology
RATIONALE AND OBJECTIVES: With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammog...

Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.

Skeletal radiology
OBJECTIVE: Radiographic bone age assessment (BAA) is used in the evaluation of pediatric endocrine and metabolic disorders. We previously developed an automated artificial intelligence (AI) deep learning algorithm to perform BAA using convolutional n...

Deep Convolutional Neural Networks for Chest Diseases Detection.

Journal of healthcare engineering
Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many me...