AIMC Topic:
Image Interpretation, Computer-Assisted
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Classification of acute lymphoblastic leukemia using deep learning.
Microscopy research and technique
Oct 23, 2018
Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose A...
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.
IEEE transactions on medical imaging
Oct 22, 2018
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o...
Sparse Representation Over Learned Dictionaries on the Riemannian Manifold for Automated Grading of Nuclear Pleomorphism in Breast Cancer.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Oct 22, 2018
Breast cancer is found to be the most pervasive type of cancer among women. Computer aided detection and diagnosis of cancer at the initial stages can increase the chances of recovery and thus reduce the mortality rate through timely prognosis and ad...
Deep neural network improves fracture detection by clinicians.
Proceedings of the National Academy of Sciences of the United States of America
Oct 22, 2018
Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often ha...
A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.
BMC veterinary research
Oct 22, 2018
BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, an...
Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images.
Physiological measurement
Oct 22, 2018
OBJECTIVE: Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated...
Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph.
IEEE journal of biomedical and health informatics
Oct 19, 2018
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to in...
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers.
Medical image analysis
Oct 19, 2018
Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitt...
SCREEN-DR: Collaborative platform for diabetic retinopathy.
International journal of medical informatics
Oct 18, 2018
BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible visual loss. Screening programs, based on retinal imaging techniques, are fundamental to detect the...
State of the Art: Machine Learning Applications in Glioma Imaging.
AJR. American journal of roentgenology
Oct 17, 2018
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MR...
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