AIMC Topic: Deep Learning

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Artery-vein segmentation in fundus images using a fully convolutional network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Epidemiological studies demonstrate that dimensions of retinal vessels change with ocular diseases, coronary heart disease and stroke. Different metrics have been described to quantify these changes in fundus images, with arteriolar and venular calib...

Self-supervised iterative refinement learning for macular OCT volumetric data classification.

Computers in biology and medicine
We present self-supervised iterative refinement learning (SIRL) as a pipeline to improve a type of macular optical coherence tomography (OCT) volumetric image classification algorithms. In this type of algorithms, first, two-dimensional (2D) image cl...

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).

IEEE transactions on medical imaging
In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enfor...

Microvascularity detection and quantification in glioma: a novel deep-learning-based framework.

Laboratory investigation; a journal of technical methods and pathology
Microvascularity is highly correlated with the grading and subtyping of gliomas, making this one of its most important histological features. Accurate quantitative analysis of microvessels is helpful for the development of a targeted therapy for anti...

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance ...

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

International journal of radiation oncology, biology, physics
PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation de...

Deep Learning-Assisted Three-Dimensional Fluorescence Difference Spectroscopy for Identification and Semiquantification of Illicit Drugs in Biofluids.

Analytical chemistry
The fast identification and quantification of illicit drugs in biofluids are of great significance in clinical detection. However, existing drug detection strategies cannot fully meet clinical needs, and the on-site identification and quantification ...