AIMC Topic: Image Interpretation, Computer-Assisted

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Metastasis detection from whole slide images using local features and random forests.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

NeuroImage
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is ...

Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection.

Computers in biology and medicine
Different types of breast cancer are affecting lives of women across the world. Common types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). While...

Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

Medical image analysis
Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases...

Gaze-contingent perceptually enabled interactions in the operating theatre.

International journal of computer assisted radiology and surgery
PURPOSE: Improved surgical outcome and patient safety in the operating theatre are constant challenges. We hypothesise that a framework that collects and utilises information -especially perceptually enabled ones-from multiple sources, could help to ...

Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method.

Computers in biology and medicine
BACKGROUND: Celiac disease is one of the most common diseases in the world. Capsule endoscopy is an alternative way to visualize the entire small intestine without invasiveness to the patient. It is useful to characterize celiac disease, but hours ar...

A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.

Computational and mathematical methods in medicine
The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet trans...

Automated Identification of Diabetic Retinopathy Using Deep Learning.

Ophthalmology
PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The obje...

A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks.

Computers in biology and medicine
Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning ...