AIMC Topic: Image Processing, Computer-Assisted

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Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks.

Journal of digital imaging
Automatic segmentation of the retinal vasculature and the optic disc is a crucial task for accurate geometric analysis and reliable automated diagnosis. In recent years, Convolutional Neural Networks (CNN) have shown outstanding performance compared ...

[Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images.

Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas.

Clinical and translational gastroenterology
OBJECTIVES: Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic adenocarcinoma. Artificial intelligence (AI) is a mathematical concept whose implementation automates learning and recognizing data patterns. The aim of ...

A new method using machine learning for automated image analysis applied to chip-based digital assays.

The Analyst
Chip-based digital assays such as the digital polymerase chain reaction (digital PCR), digital loop-mediated amplification (digital LAMP), digital enzyme-linked immunosorbent assay (digital ELISA) and digital proximity ligation assay (digital PLA) ne...

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

JCO clinical cancer informatics
PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the applicati...

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Journal of thoracic imaging
Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promis...

Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.

Korean journal of radiology
OBJECTIVE: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breas...

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

Biomedizinische Technik. Biomedical engineering
PURPOSE: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance ...