AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part ...

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Medical image analysis
Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intr...

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection.

Neural networks : the official journal of the International Neural Network Society
Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as pos...

Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.

JAMA network open
IMPORTANCE: Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow.

A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules.

Asian Pacific journal of cancer prevention : APJCP
In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extrac...

Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.

Journal of biomedical informatics
We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic...

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Computational and mathematical methods in medicine
Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvat...

ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques.

Journal of medical systems
As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes...

RAMS: Remote and automatic mammogram screening.

Computers in biology and medicine
About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, the...