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Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling.
IEEE transactions on bio-medical engineering
Feb 15, 2016
GOAL: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise labeled data to train various supervised machine learning models. However, it is a tedious and time-consuming work to collect sufficient precisely labeled image...
Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.
Regulatory toxicology and pharmacology : RTP
Feb 13, 2016
Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guidelin...
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.
IEEE transactions on medical imaging
Feb 11, 2016
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image fea...
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks.
IEEE transactions on medical imaging
Feb 11, 2016
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelle...
Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.
Scientific reports
Feb 11, 2016
We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to co...
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.
IEEE transactions on medical imaging
Feb 8, 2016
Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is...
Computerized "Learn-As-You-Go" classification of traumatic brain injuries using NEISS narrative data.
Accident; analysis and prevention
Feb 3, 2016
One important routine task in injury research is to effectively classify injury circumstances into user-defined categories when using narrative text. However, traditional manual processes can be time consuming, and existing batch learning systems can...
TensorFlow: Biology's Gateway to Deep Learning?
Cell systems
Jan 27, 2016
TensorFlow is Google's recently released open-source software for deep learning. What are its applications for computational biology?
A Natural Language Interface Concordant with a Knowledge Base.
Computational intelligence and neuroscience
Jan 21, 2016
The discordance between expressions interpretable by a natural language interface (NLI) system and those answerable by a knowledge base is a critical problem in the field of NLIs. In order to solve this discordance problem, this paper proposes a meth...
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