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https://www.readbyqxmd.com/read/28942485/crowdsourcing-and-automated-retinal-image-analysis-for-diabetic-retinopathy
#1
REVIEW
Lucy I Mudie, Xueyang Wang, David S Friedman, Christopher J Brady
PURPOSE OF REVIEW: As the number of people with diabetic retinopathy (DR) in the USA is expected to increase threefold by 2050, the need to reduce health care costs associated with screening for this treatable disease is ever present. Crowdsourcing and automated retinal image analysis (ARIA) are two areas where new technology has been applied to reduce costs in screening for DR. This paper reviews the current literature surrounding these new technologies. RECENT FINDINGS: Crowdsourcing has high sensitivity for normal vs abnormal images; however, when multiple categories for severity of DR are added, specificity is reduced...
September 23, 2017: Current Diabetes Reports
https://www.readbyqxmd.com/read/28941550/interaction-prediction-in-structure-based-virtual-screening-using-deep-learning
#2
Adam Gonczarek, Jakub M Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, Michał J Walczak
We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each molecule separately. These fingerprints are further non-linearly transformed, their inner product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning...
September 14, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28939812/efficient-representation-of-quantum-many-body-states-with-deep-neural-networks
#3
Xun Gao, Lu-Ming Duan
Part of the challenge for quantum many-body problems comes from the difficulty of representing large-scale quantum states, which in general requires an exponentially large number of parameters. Neural networks provide a powerful tool to represent quantum many-body states. An important open question is what characterizes the representational power of deep and shallow neural networks, which is of fundamental interest due to the popularity of deep learning methods. Here, we give a proof that, assuming a widely believed computational complexity conjecture, a deep neural network can efficiently represent most physical states, including the ground states of many-body Hamiltonians and states generated by quantum dynamics, while a shallow network representation with a restricted Boltzmann machine cannot efficiently represent some of those states...
September 22, 2017: Nature Communications
https://www.readbyqxmd.com/read/28939354/a-deep-learning-approach-to-estimate-chemically-treated-collagenous-tissue-nonlinear-anisotropic-stress-strain-responses-from-microscopy-images
#4
Liang Liang, Minliang Liu, Wei Sun
Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen to develop a representative application...
September 19, 2017: Acta Biomaterialia
https://www.readbyqxmd.com/read/28938130/learning-in-the-machine-the-symmetries-of-the-deep-learning-channel
#5
Pierre Baldi, Peter Sadowski, Zhiqin Lu
In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules...
September 5, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28937281/tracking-by-detection-of-surgical-instruments-in-minimally-invasive-surgery-via-the-convolutional-neural-network-deep-learning-based-method
#6
Zijian Zhao, Sandrine Voros, Ying Weng, Faliang Chang, Ruijian Li
BACKGROUND: Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. METHODS: A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one...
September 22, 2017: Computer Assisted Surgery (Abingdon, England)
https://www.readbyqxmd.com/read/28936494/detection-of-intracranial-hypertension-using-deep-learning
#7
Benjamin Quachtran, Robert Hamilton, Fabien Scalzo
Intracranial Hypertension, a disorder characterized by elevated pressure in the brain, is typically monitored in neurointensive care and diagnosed only after elevation has occurred. This reaction-based method of treatment leaves patients at higher risk of additional complications in case of misdetection. The detection of intracranial hypertension has been the subject of many recent studies in an attempt to accurately characterize the causes of hypertension, specifically examining waveform morphology. We investigate the use of Deep Learning, a hierarchical form of machine learning, to model the relationship between hypertension and waveform morphology, giving us the ability to accurately detect presence hypertension...
December 2016: Proceedings of the ... IAPR International Conference on Pattern Recognition
https://www.readbyqxmd.com/read/28935940/layer-and-cell-type-selective-co-transmission-by-a-basal-forebrain-cholinergic-projection-to-the-olfactory-bulb
#8
Daniel T Case, Shawn D Burton, Jeremy Y Gedeon, Sean-Paul G Williams, Nathaniel N Urban, Rebecca P Seal
Cholinergic neurons in the basal forebrain project heavily to the main olfactory bulb, the first processing station in the olfactory pathway. The projections innervate multiple layers of the main olfactory bulb and strongly influence odor discrimination, detection, and learning. The precise underlying circuitry of this cholinergic input to the main olfactory bulb remains unclear, however. Here, we identify a specific basal forebrain cholinergic projection that innervates select neurons concentrated in the internal plexiform layer of the main olfactory bulb...
September 21, 2017: Nature Communications
https://www.readbyqxmd.com/read/28934254/deep-reconstruction-model-for-dynamic-pet-images
#9
Jianan Cui, Xin Liu, Yile Wang, Huafeng Liu
Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the activity distribution-based on individual frames, may lead to inaccurate results due to the checkerboard effect and limitation of photon counts. In this paper, we propose a stacked sparse auto-encoder based reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a deep learning representation, where the encoding layers extract the prototype features, such as edges, so that, in the decoding layers, the reconstructed results are obtained through a combination of those features...
2017: PloS One
https://www.readbyqxmd.com/read/28932991/automated-reference-free-detection-of-motion-artifacts-in-magnetic-resonance-images
#10
Thomas Küstner, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Bin Yang, Fritz Schick, Sergios Gatidis
OBJECTIVES: Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. MATERIALS AND METHODS: T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation...
September 20, 2017: Magma
https://www.readbyqxmd.com/read/28932980/understanding-clinical-mammographic-breast-density-assessment-a-deep-learning-perspective
#11
Aly A Mohamed, Yahong Luo, Hong Peng, Rachel C Jankowitz, Shandong Wu
Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view...
September 20, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28932180/hardware-efficient-on-line-learning-through-pipelined-truncated-error-backpropagation-in-binary-state-networks
#12
Hesham Mostafa, Bruno Pedroni, Sadique Sheik, Gert Cauwenberghs
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28931888/automatic-classification-of-cancerous-tissue-in-laserendomicroscopy-images-of-the-oral-cavity-using-deep-learning
#13
Marc Aubreville, Christian Knipfer, Nicolai Oetter, Christian Jaremenko, Erik Rodner, Joachim Denzler, Christopher Bohr, Helmut Neumann, Florian Stelzle, Andreas Maier
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis...
September 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28931601/local-cellular-neighbourhood-controls-proliferation-in-cell-competition
#14
Anna Bove, Daniel Gradeci, Yasuyuki Fujita, Shiladitya Banerjee, Guillaume Charras, Alan R Lowe
Cell competition is a quality control mechanism through which tissues eliminate unfit cells. Cell competition can result from short-range biochemical inductions or long-range mechanical cues. However, little is known about how cell-scale interactions give rise to population shifts in tissues, due to the lack of experimental and computational tools to efficiently characterise interactions at the single-cell level. Here, we address these challenges by combining long-term automated microscopy with deep learning image analysis to decipher how single-cell behaviour determines tissue make-up during competition...
September 20, 2017: Molecular Biology of the Cell
https://www.readbyqxmd.com/read/28930381/intraocular-pressure-and-big-bubble-diameter-in-deep-anterior-lamellar-keratoplasty-an-ex-vivo-microscope-integrated-oct-with-heads-up-display-study
#15
Paramjit K Bhullar, Oscar M Carrasco-Zevallos, Alexandria Dandridge, Neel D Pasricha, Brenton Keller, Liangbo Shen, Joseph A Izatt, Cynthia A Toth, Anthony N Kuo
PURPOSE: To investigate the relationship between intraocular pressure (IOP) and big bubble (BB) formation in a model of deep anterior lamellar keratoplasty (DALK). DESIGN: Ex-vivo. METHODS: Corneoscleral buttons from human donors were loaded onto an artificial anterior chamber connected to a column of balanced salt solution. A surgeon-in-training learned to perform DALK via the BB technique using swept-source microscope-integrated optical coherence tomography (SS-MIOCT) with heads-up display (HUD)...
September 2017: Asia-Pacific Journal of Ophthalmology
https://www.readbyqxmd.com/read/28928947/puzzles-in-modern-biology-v-why-are-genomes-overwired
#16
Steven A Frank
Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added...
2017: F1000Research
https://www.readbyqxmd.com/read/28926310/coronary-ct-angiography-derived-fractional-flow-reserve
#17
Christian Tesche, Carlo N De Cecco, Moritz H Albrecht, Taylor M Duguay, Richard R Bayer, Sheldon E Litwin, Daniel H Steinberg, U Joseph Schoepf
Invasive coronary angiography (ICA) with measurement of fractional flow reserve (FFR) by means of a pressure wire technique is the established reference standard for the functional assessment of coronary artery disease (CAD) ( 1 , 2 ). Coronary computed tomographic (CT) angiography has emerged as a noninvasive method for direct assessment of CAD and plaque characterization with high diagnostic accuracy compared with ICA ( 3 , 4 ). However, the solely anatomic assessment provided with both coronary CT angiography and ICA has poor discriminatory power for ischemia-inducing lesions...
October 2017: Radiology
https://www.readbyqxmd.com/read/28925823/deep-learning-mr-imaging-based-attenuation-correction-for-pet-mr-imaging
#18
Fang Liu, Hyungseok Jang, Richard Kijowski, Tyler Bradshaw, Alan B McMillan
Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training...
September 19, 2017: Radiology
https://www.readbyqxmd.com/read/28924815/automatic-determination-of-the-need-for-intravenous-contrast-in-musculoskeletal-mri-examinations-using-ibm-watson-s-natural-language-processing-algorithm
#19
Hari Trivedi, Joseph Mesterhazy, Benjamin Laguna, Thienkhai Vu, Jae Ho Sohn
Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors...
September 18, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28924576/deep-learning-in-breast-cancer-risk-assessment-evaluation-of-convolutional-neural-networks-on-a-clinical-dataset-of-full-field-digital-mammograms
#20
Hui Li, Maryellen L Giger, Benjamin Q Huynh, Natalia O Antropova
To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases)...
October 2017: Journal of Medical Imaging
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