AIMC Topic: Image Interpretation, Computer-Assisted

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Automatic polyp frame screening using patch based combined feature and dictionary learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned proble...

Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features.

IEEE transactions on bio-medical engineering
In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very de...

Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review.

Computers in biology and medicine
PURPOSE OF REVIEW: Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and ...

Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches.

International journal of medical informatics
BACKGROUND: The wide adoption of electronic health record systems (EHRs) in hospitals in China has made large amounts of data available for clinical research including breast cancer. Unfortunately, much of detailed clinical information is embedded in...

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.

IEEE transactions on medical imaging
Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance...

Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks.

IEEE transactions on medical imaging
Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In c...

Regional Multi-View Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients.

IEEE transactions on bio-medical engineering
OBJECTIVE: The aim of this paper is to describe an automated diagnostic pipeline that uses as input only ultrasound (US) data, but is at the same time informed by a training database of multimodal magnetic resonance (MR) and US image data.

Automated Analysis for Retinopathy of Prematurity by Deep Neural Networks.

IEEE transactions on medical imaging
Retinopathy of Prematurity (ROP) is a retinal vasproliferative disorder disease principally observed in infants born prematurely with low birth weight. ROP is an important cause of childhood blindness. Although automatic or semi-automatic diagnosis o...

Building medical image classifiers with very limited data using segmentation networks.

Medical image analysis
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem,...