AIMC Topic:
Image Interpretation, Computer-Assisted
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DeepPap: Deep Convolutional Networks for Cervical Cell Classification.
IEEE journal of biomedical and health informatics
May 19, 2017
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most trad...
A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition.
IEEE journal of biomedical and health informatics
May 17, 2017
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, conside...
Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study.
Magnetic resonance in medicine
May 14, 2017
PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied usi...
Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 5, 2017
This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a ...
Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.
G3 (Bethesda, Md.)
May 5, 2017
High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a ta...
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.
BMC medical imaging
May 4, 2017
BACKGROUND: Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit ...
Deep image mining for diabetic retinopathy screening.
Medical image analysis
Apr 28, 2017
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert...
Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.
AJNR. American journal of neuroradiology
Apr 27, 2017
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purp...
Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.
Eye (London, England)
Apr 21, 2017
PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five ste...
Metastasis detection from whole slide images using local features and random forests.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Apr 20, 2017
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...
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