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https://www.readbyqxmd.com/read/29916118/emerging-intraoperative-imaging-modalities-to-improve-surgical-precision
#1
Israt S Alam, Idan Steinberg, Ophir Vermesh, Nynke S van den Berg, Eben L Rosenthal, Gooitzen M van Dam, Vasilis Ntziachristos, Sanjiv S Gambhir, Sophie Hernot, Stephan Rogalla
Intraoperative imaging (IOI) is performed to guide delineation and localization of regions of surgical interest. While oncological surgical planning predominantly utilizes x-ray computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US), intraoperative guidance mainly remains on surgeon interpretation and pathology for confirmation. Over the past decades however, intraoperative guidance has evolved significantly with the emergence of several novel imaging technologies, including fluorescence-, Raman, photoacoustic-, and radio-guided approaches...
June 18, 2018: Molecular Imaging and Biology: MIB: the Official Publication of the Academy of Molecular Imaging
https://www.readbyqxmd.com/read/29915783/computational-chemical-synthesis-analysis-and-pathway-design
#2
REVIEW
Fan Feng, Luhua Lai, Jianfeng Pei
With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules...
2018: Frontiers in Chemistry
https://www.readbyqxmd.com/read/29915490/my-tryst-of-writing-and-publishing-a-comprehensive-medical-textbook-in-vernacular-hindi-and-new-hindi-medical-terminology
#3
Trilok Chandra Goel, Apul Goel, Sandeep Kumar
In India, although the native language is not English but the medical education is imparted in English. The authors have written a textbook of surgery in Hindi with the intention of promoting the understanding of surgery and encouraging reflective and deep learning for students whose native language is Hindi. In this article, the authors share experiences of writing such a book, the reasons for the same and also discuss the creation of new medical nomenclature in Hindi.
April 2018: Indian Journal of Surgery
https://www.readbyqxmd.com/read/29915334/highly-accurate-model-for-prediction-of-lung-nodule-malignancy-with-ct-scans
#4
Jason L Causey, Junyu Zhang, Shiqian Ma, Bo Jiang, Jake A Qualls, David G Politte, Fred Prior, Shuzhong Zhang, Xiuzhen Huang
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort...
June 18, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29914866/diagnostic-accuracy-of-a-machine-learning-approach-to-coronary-computed-tomographic-angiography-based-fractional-flow-reserve-result-from-the-machine-consortium
#5
Adriaan Coenen, Young-Hak Kim, Mariusz Kruk, Christian Tesche, Jakob De Geer, Akira Kurata, Marisa L Lubbers, Joost Daemen, Lucian Itu, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Anders Persson, U Joseph Schoepf, Cezary Kepka, Dong Hyun Yang, Koen Nieman
BACKGROUND: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding...
June 2018: Circulation. Cardiovascular Imaging
https://www.readbyqxmd.com/read/29914357/algorithms-designed-for-compressed-gene-data-transformation-among-gene-banks-with-different-references
#6
Qiuming Luo, Chao Guo, Yi Jun Zhang, Ye Cai, Gang Liu
BACKGROUND: With the reduction of gene sequencing cost and demand for emerging technologies such as precision medical treatment and deep learning in genome, it is an era of gene data outbreaks today. How to store, transmit and analyze these data has become a hotspot in the current research. Now the compression algorithm based on reference is widely used due to its high compression ratio. There exists a big problem that the data from different gene banks can't merge directly and share information efficiently, because these data are usually compressed with different references...
June 18, 2018: BMC Bioinformatics
https://www.readbyqxmd.com/read/29914314/dynamic-redistribution-of-plasticity-in-a-cerebellar-spiking-neural-network-reproducing-an-associative-learning-task-perturbed-by-tms
#7
Alberto Antonietti, Jessica Monaco, Egidio D'Angelo, Alessandra Pedrocchi, Claudia Casellato
During natural learning, synaptic plasticity is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks evolving under behavioral feedback and should involve changes distributed across multiple synaptic sites. In eyeblink classical conditioning (EBCC), the cerebellum learns to predict the precise timing between two stimuli, hence EBCC represents an elementary yet meaningful paradigm to investigate the cerebellar network functioning. We have simulated EBCC mechanisms by reconstructing a realistic cerebellar microcircuit model and embedding multiple plasticity rules imitating those revealed experimentally...
April 24, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29914127/cloud-based-behavioral-monitoring-in-smart-homes
#8
Niccolò Mora, Guido Matrella, Paolo Ciampolini
Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data...
June 15, 2018: Sensors
https://www.readbyqxmd.com/read/29908902/a-study-of-generalizability-of-recurrent-neural-network-based-predictive-models-for-heart-failure-onset-risk-using-a-large-and-heterogeneous-ehr-data-set
#9
Laila R Bekhet, Yonghui Wu, Ningtao Wang, Xin Geng, Wenjin Jim Zheng, Fei Wang, Hulin Wu, Hua Xu, Degui Zhi
Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals...
June 14, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29908156/classifying-the-molecular-functions-of-rab-gtpases-in-membrane-trafficking-using-deep-convolutional-neural-networks
#10
Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell...
June 13, 2018: Analytical Biochemistry
https://www.readbyqxmd.com/read/29906949/scale-invariant-feature-extraction-of-neural-network-and-renormalization-group-flow
#11
Satoshi Iso, Shotaro Shiba, Sumito Yokoo
Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM...
May 2018: Physical Review. E
https://www.readbyqxmd.com/read/29905680/end-to-end-deep-neural-network-for-optical-inversion-in-quantitative-photoacoustic-imaging
#12
Chuangjian Cai, Kexin Deng, Cheng Ma, Jianwen Luo
An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging. A residual learning framework is used to facilitate optimization and to gain better accuracy from considerably increased network depth. The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO2 ). According to our numerical experiments, the estimations of sO2 and indocyanine green concentration are accurate and robust against variations in both optical property and object geometry...
June 15, 2018: Optics Letters
https://www.readbyqxmd.com/read/29904551/curricular-activities-that-promote-metacognitive-skills-impact-lower-performing-students-in-an-introductory-biology-course
#13
Nathan V Dang, Jacob C Chiang, Heather M Brown, Kelly K McDonald
This study explores the impacts of repeated curricular activities designed to promote metacognitive skills development and academic achievement on students in an introductory biology course. Prior to this study, the course curriculum was enhanced with pre-assignments containing comprehension monitoring and self-evaluation questions, exam review assignments with reflective questions related to study habits, and an optional opportunity for students to explore metacognition and deep versus surface learning. We used a mixed-methods study design and collected data over two semesters...
2018: Journal of Microbiology & Biology Education: JMBE
https://www.readbyqxmd.com/read/29904135/a-deep-learning-framework-to-discern-and-count-microscopic-nematode-eggs
#14
Adedotun Akintayo, Gregory L Tylka, Asheesh K Singh, Baskar Ganapathysubramanian, Arti Singh, Soumik Sarkar
In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybean yield loss is negatively correlated with the density of SCN eggs that are present in the soil. While there has been progress in automating extraction of egg-filled cysts and eggs from soil samples counting SCN eggs obtained from soil samples using computer vision techniques has proven to be an extremely difficult challenge...
June 14, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29903491/fine-grained-leukocyte-classification-with-deep-residual-learning-for-microscopic-images
#15
Feiwei Qin, Nannan Gao, Yong Peng, Zizhao Wu, Shuying Shen, Artur Grudtsin
BACKGROUND AND OBJECTIVE: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories...
August 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29903489/skin-lesion-segmentation-in-dermoscopy-images-via-deep-full-resolution-convolutional-networks
#16
Mohammed A Al-Masni, Mugahed A Al-Antari, Mun-Taek Choi, Seung-Moo Han, Tae-Seong Kim
BACKGROUND AND OBJECTIVE: Automatic segmentation of skin lesions in dermoscopy images is still a challenging task due to the large shape variations and indistinct boundaries of the lesions. Accurate segmentation of skin lesions is a key prerequisite step for any computer-aided diagnostic system to recognize skin melanoma. METHODS: In this paper, we propose a novel segmentation methodology via full resolution convolutional networks (FrCN). The proposed FrCN method directly learns the full resolution features of each individual pixel of the input data without the need for pre- or post-processing operations such as artifact removal, low contrast adjustment, or further enhancement of the segmented skin lesion boundaries...
August 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29903476/convolutional-neural-network-based-pso-for-lung-nodule-false-positive-reduction-on-ct-images
#17
Giovanni Lucca França da Silva, Thales Levi Azevedo Valente, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass
BACKGROUND AND OBJECTIVE: Detection of lung nodules is critical in CAD systems; this is because of their similar contrast with other structures and low density, which result in the generation of numerous false positives (FPs). Therefore, this study proposes a methodology to reduce the FP number using a deep learning technique in conjunction with an evolutionary technique. METHOD: The particle swarm optimization (PSO) algorithm was used to optimize the network hyperparameters in the convolutional neural network (CNN) in order to enhance the network performance and eliminate the requirement of manual search...
August 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29903454/physiology-of-the-cerebellum
#18
Egidio D'Angelo
The cerebellum is a central brain structure deeply integrated into major loops with the cerebral cortex, brainstem, and spinal cord. The cerebellum shows a complex regional organization consisting of modules with sagittal orientation. The cerebellum takes part in motor control and its lesions cause a movement incoordination syndrome called ataxia. Recent observations also imply involvement of the cerebellum in cognition and executive control, with an impact on pathologies like dyslexia and autism. The cerebellum operates as a forward controller learning to predict the precise timing of correlated events...
2018: Handbook of Clinical Neurology
https://www.readbyqxmd.com/read/29901853/deep-learning-based-computer-aided-classifier-developed-with-a-small-dataset-of-clinical-images-surpasses-board-certified-dermatologists-in-skin-tumor-diagnosis
#19
Y Fujisawa, Y Otomo, Y Ogata, Y Nakamura, R Fujita, Y Ishitsuka, R Watanabe, N Okiyama, K Ohara, M Fujimoto
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve skin cancer screening sensitivity and specificity, but the number of training images required for such system is thought to be extremely large. OBJECTIVE: To determine if deep-learning technology could be used to develop an efficient skin cancer classifying system with a relatively small dataset of clinical images. METHODS: A deep convolutional neural network (DCNN) was trained using a dataset of 4867 clinical images obtained from 1842 patients diagnosed with skin tumors at the University of Tsukuba Hospital from 2003 to 2016...
June 14, 2018: British Journal of Dermatology
https://www.readbyqxmd.com/read/29901440/thermal-augmented-expression-recognition
#20
Shangfei Wang, Bowen Pan, Huaping Chen, Qiang Ji
Visible facial images provide geometric and appearance patterns of facial expressions and are sensitive to illumination changes. Thermal facial images record facial temperature distribution and are robust to light conditions. Therefore, expression recognition is enhanced by visible and thermal image fusion. In most cases, only visible images are available due to the widespread popularity of visible cameras and the high cost of thermal cameras. Thus, we propose a novel visible expression recognition method by using thermal infrared (IR) data as privileged information, which is only available during training...
July 2018: IEEE Transactions on Cybernetics
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