AIMC Topic: Diagnostic Imaging
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Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Data augmentation is an essential part of training discriminative Convolutional Neural Networks (CNNs). A variety of augmentation strategies, including horizontal flips, random crops, and principal component analysis (PCA), have been proposed and sho...
Radiomics in radiooncology - Challenging the medical physicist.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Mar 27, 2018
PURPOSE: Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implem...
Data Analysis Strategies in Medical Imaging.
Clinical cancer research : an official journal of the American Association for Cancer Research
Mar 26, 2018
Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence has allowed for detailed quantification of radiographic characteristics of tissues using predefined ...
An application of cascaded 3D fully convolutional networks for medical image segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 16, 2018
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical st...
Assessing microscope image focus quality with deep learning.
BMC bioinformatics
Mar 15, 2018
BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with hig...
Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis.
Computers in biology and medicine
Mar 12, 2018
Automated biomedical image classification could confront the challenges of high level noise, image blur, illumination variation and complicated geometric correspondence among various categorical biomedical patterns in practice. To handle these challe...
Deep Learning and Its Applications in Biomedicine.
Genomics, proteomics & bioinformatics
Mar 6, 2018
Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the und...
Deep learning with convolutional neural network in radiology.
Japanese journal of radiology
Mar 1, 2018
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the...
Microaneurysm detection using fully convolutional neural networks.
Computer methods and programs in biomedicine
Feb 22, 2018
BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automat...
A novel biomedical image indexing and retrieval system via deep preference learning.
Computer methods and programs in biomedicine
Feb 6, 2018
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an...
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