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https://www.readbyqxmd.com/read/29470172/hookworm-detection-in-wireless-capsule-endoscopy-images-with-deep-learning
#1
Jun-Yan He, Xiao Wu, Yu-Gang Jiang, Qiang Peng, Ramesh Jain
As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been applied to automatic hookworm detection. Unfortunately, it remains a challenging task. In recent years, deep convolutional neural network (CNN) has demonstrated impressive performance in various image and video analysis tasks. In this paper, a novel deep hookworm detection framework is proposed for WCE images, which simultaneously models visual appearances and tubular patterns of hookworms...
May 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29469107/deep-learning-for-biology
#2
Sarah Webb
No abstract text is available yet for this article.
February 22, 2018: Nature
https://www.readbyqxmd.com/read/29468607/the-patient-reported-information-multidimensional-exploration-prime-framework-for-investigating-emotions-and-other-factors-of-prostate-cancer-patients-with-low-intermediate-risk-based-on-online-cancer-support-group-discussions
#3
Tharindu Bandaragoda, Weranja Ranasinghe, Achini Adikari, Daswin de Silva, Nathan Lawrentschuk, Damminda Alahakoon, Raj Persad, Damien Bolton
BACKGROUND: This study aimed to use the Patient Reported Information Multidimensional Exploration (PRIME) framework, a novel ensemble of machine-learning and deep-learning algorithms, to extract, analyze, and correlate self-reported information from Online Cancer Support Groups (OCSG) by patients (and partners of patients) with low intermediate-risk prostate cancer (PCa) undergoing radical prostatectomy (RP), external beam radiotherapy (EBRT), and active surveillance (AS), and to investigate its efficacy in quality-of-life (QoL) and emotion measures...
February 21, 2018: Annals of Surgical Oncology
https://www.readbyqxmd.com/read/29467373/deep-learning-based-tissue-analysis-predicts-outcome-in-colorectal-cancer
#4
Dmitrii Bychkov, Nina Linder, Riku Turkki, Stig Nordling, Panu E Kovanen, Clare Verrill, Margarita Walliander, Mikael Lundin, Caj Haglund, Johan Lundin
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available...
February 21, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29467363/enhancing-hi-c-data-resolution-with-deep-convolutional-neural-network-hicplus
#5
Yan Zhang, Lin An, Jie Xu, Bo Zhang, W Jim Zheng, Ming Hu, Jijun Tang, Feng Yue
Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads...
February 21, 2018: Nature Communications
https://www.readbyqxmd.com/read/29464432/sharpness-aware-low-dose-ct-denoising-using-conditional-generative-adversarial-network
#6
Xin Yi, Paul Babyn
Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process...
February 20, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29464316/the-right-look-for-the-job-decoding-cognitive-processes-involved-in-the-task-from-spatial-eye-movement-patterns
#7
Magdalena Ewa Król, Michał Król
The aim of the study was not only to demonstrate whether eye-movement-based task decoding was possible but also to investigate whether eye-movement patterns can be used to identify cognitive processes behind the tasks. We compared eye-movement patterns elicited under different task conditions, with tasks differing systematically with regard to the types of cognitive processes involved in solving them. We used four tasks, differing along two dimensions: spatial (global vs. local) processing (Navon, Cognit Psychol, 9(3):353-383 1977) and semantic (deep vs...
February 20, 2018: Psychological Research
https://www.readbyqxmd.com/read/29464026/converging-blockchain-and-next-generation-artificial-intelligence-technologies-to-decentralize-and-accelerate-biomedical-research-and-healthcare
#8
Polina Mamoshina, Lucy Ojomoko, Yury Yanovich, Alex Ostrovski, Alex Botezatu, Pavel Prikhodko, Eugene Izumchenko, Alexander Aliper, Konstantin Romantsov, Alexander Zhebrak, Iraneus Obioma Ogu, Alex Zhavoronkov
The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have...
January 19, 2018: Oncotarget
https://www.readbyqxmd.com/read/29463200/poggio-t-a-anselmi-f-visual-cortex-and-deep-networks-learning-invariant-representations-poggio-t-a-anselmi-f-visual-cortex-and-deep-networks-learning-invariant-representations-cambridge-ma-mit-press-2016-136-pp-isbn-9780262034722-%C3%A2-26-95-hardback
#9
https://www.readbyqxmd.com/read/29462625/enhanced-prediction-of-recombination-hotspots-using-input-features-extracted-by-class-specific-autoencoders
#10
Abhigyan Nath, S Karthikeyan
In yeast and in some mammals the frequencies of recombination are high in some genomic locations which are known as recombination hotspots and in the locations where the recombination is below average are consequently known as coldspots. Knowledge of the hotspot regions gives clues about understanding the meiotic process and also in understanding the possible effects of sequence variation in these regions. Moreover, accurate information about the hotspot and coldspot regions can reveal insights into the genome evolution...
February 17, 2018: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/29461955/deep-learning-of-radiology-reports-for-pulmonary-embolus-is-a-computer-reading-my-report
#11
Elizabeth A Krupinski
No abstract text is available yet for this article.
March 2018: Radiology
https://www.readbyqxmd.com/read/29458680/colony-analysis-and-deep-learning-uncover-5-hydroxyindole-as-an-inhibitor-of-gliding-motility-and-iridescence-in-cellulophaga-lytica
#12
Maylis Chapelais-Baron, Isabelle Goubet, Renaud Péteri, Maria de Fatima Pereira, Tâm Mignot, Apolline Jabveneau, Eric Rosenfeld
Iridescence is an original type of colouration that is relatively widespread in nature but has been either incompletely described or entirely neglected in prokaryotes. Recently, we reported a brilliant 'pointillistic' iridescence in agar-grown colony biofilms of Cellulophaga lytica and some other marine Flavobacteria that exhibit gliding motility. Bacterial iridescence is created by a unique self-organization of sub-communities of cells, but the mechanisms underlying such living photonic crystals are unknown...
February 5, 2018: Microbiology
https://www.readbyqxmd.com/read/29457314/latent-source-mining-in-fmri-via-restricted-boltzmann-machine
#13
Xintao Hu, Heng Huang, Bo Peng, Junwei Han, Nian Liu, Jinglei Lv, Lei Guo, Christine Guo, Tianming Liu
Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set...
February 18, 2018: Human Brain Mapping
https://www.readbyqxmd.com/read/29456725/application-of-deep-learning-to-the-classification-of-images-from-colposcopy
#14
Masakazu Sato, Koji Horie, Aki Hara, Yuichiro Miyamoto, Kazuko Kurihara, Kensuke Tomio, Harushige Yokota
The objective of the present study was to investigate whether deep learning could be applied successfully to the classification of images from colposcopy. For this purpose, a total of 158 patients who underwent conization were enrolled, and medical records and data from the gynecological oncology database were retrospectively reviewed. Deep learning was performed with the Keras neural network and TensorFlow libraries. Using preoperative images from colposcopy as the input data and deep learning technology, the patients were classified into three groups [severe dysplasia, carcinoma in situ (CIS) and invasive cancer (IC)]...
March 2018: Oncology Letters
https://www.readbyqxmd.com/read/29455962/needle-localization-of-small-pulmonary-nodules-lessons-learned
#15
Patricia A Thistlethwaite, Jonathan R Gower, Moises Hernandez, Yu Zhang, Andrew C Picel, Anne C Roberts
BACKGROUND: Lung nodules that are small and deep within lung parenchyma, and have semisolid characteristics are often challenging to localize with video-assisted thoracoscopic surgery (VATS). We describe our cumulative experience using needle localization of small nodules before surgical resection. We report procedural tips, operative results, and lessons learned over time. METHODS: A retrospective review of all needle localization cases between July 1, 2006, and December 30, 2016, at a single institution was performed...
January 17, 2018: Journal of Thoracic and Cardiovascular Surgery
https://www.readbyqxmd.com/read/29455111/deepmitosis-mitosis-detection-via-deep-detection-verification-and-segmentation-networks
#16
Chao Li, Xinggang Wang, Wenyu Liu, Longin Jan Latecki
Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives...
January 31, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29455079/automatic-detection-and-segmentation-of-brain-metastases-on-multimodal-mr-images-with-a-deep-convolutional-neural-network
#17
Odelin Charron, Alex Lallement, Delphine Jarnet, Vincent Noblet, Jean-Baptiste Clavier, Philippe Meyer
Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI...
February 9, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29454006/predicting-cognitive-decline-with-deep-learning-of-brain-metabolism-and-amyloid-imaging
#18
Hongyoon Choi, Kyong Hwan Jin
For effective treatment of Alzheimer's disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. We aimed to develop an automatic image interpretation system based on a deep convolutional neural network (CNN) which can accurately predict future cognitive decline in mild cognitive impairment (MCI) patients using flurodeoxyglucose and florbetapir positron emission tomography (PET). PET images of 139 patients with AD, 171 patients with MCI and 182 normal subjects obtained from Alzheimer's Disease Neuroimaging Initiative database were used...
February 14, 2018: Behavioural Brain Research
https://www.readbyqxmd.com/read/29452923/statistical-and-machine-learning-approaches-to-predicting-protein-ligand-interactions
#19
REVIEW
Lucy J Colwell
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to design novel ligands for protein targets of interest. This review summarizes the current state of the art in this field, emphasizing the recent development of deep neural networks for predicting protein-ligand binding. We explain the major technical challenges that have caused difficulty with predicting novel ligands, including the problems of sampling noise and the challenge of using benchmark datasets that are sufficiently unbiased that they allow the model to extrapolate to new regimes...
February 13, 2018: Current Opinion in Structural Biology
https://www.readbyqxmd.com/read/29452755/machine-learning-algorithms-based-on-signals-from-a-single-wearable-inertial-sensor-can-detect-surface-and-age-related-differences-in-walking
#20
B Hu, P C Dixon, J V Jacobs, J T Dennerlein, J M Schiffman
The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen older (71.5 ± 4.2 years) and eighteen young (27.0 ± 4.7 years) healthy adults walked over flat and uneven brick surfaces wearing an inertial measurement unit (IMU) over the L5 vertebra. IMU data were binned into smaller data segments using 4-s sliding windows with 1-s step lengths...
January 12, 2018: Journal of Biomechanics
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