AIMC Topic: Workflow

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A Machine Learning-Based Raman Spectroscopic Assay for the Identification of and Related Species.

Molecules (Basel, Switzerland) Dec 10, 2019
, the causative agent of glanders, and , the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in ...
Workflow Bacterial Typing Techniques Burkholderia mallei Humans Machine Learning Spectrum Analysis, Raman Cluster Analysis
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Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.

Surgical endoscopy Dec 3, 2019
BACKGROUND: Automatic surgical workflow recognition is a key component for developing the context-aware computer-assisted surgery (CA-CAS) systems. However, automatic surgical phase recognition focused on colorectal surgery has not been reported. We ...
Humans Retrospective Studies Deep Learning Workflow Surgery, Computer-Assisted Colon, Sigmoid Colectomy Computer Systems Operative Time Laparoscopy
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Designing machine learning workflows with an application to topological data analysis.

PloS one Dec 2, 2019
In this paper we define the concept of the Machine Learning Morphism (MLM) as a fundamental building block to express operations performed in machine learning such as data preprocessing, feature extraction, and model training. Inspired by statistical...
Data Mining Machine Learning Data Analysis Workflow
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Ethical considerations in artificial intelligence.

European journal of radiology Nov 23, 2019
With artificial intelligence (AI) precipitously perched at the apex of the hype curve, the promise of transforming the disparate fields of healthcare, finance, journalism, and security and law enforcement, among others, is enormous. For healthcare - ...
Forecasting Radiology Radiologists Workflow Humans Artificial Intelligence
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ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.

EBioMedicine Nov 22, 2019
BACKGROUND: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". Ho...
Web Browser Adenocarcinoma of Lung Humans Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Software Neoplasm Staging Reproducibility of Results Lung Neoplasms Algorithms Workflow Female Histocytochemistry Tumor Microenvironment Male
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Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

EBioMedicine Nov 15, 2019
BACKGROUND: Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfortunately, postoperative recurrence rate remains high and there is no reliable prediction tool. We exp...
Neoplasm Recurrence, Local Hepatectomy Proportional Hazards Models Image Processing, Computer-Assisted Contrast Media Workflow Radiographic Image Enhancement Retrospective Studies Tomography, X-Ray Computed Female Male Liver Neoplasms Carcinoma, Hepatocellular Machine Learning Algorithms Prognosis Humans
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CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.

Scientific reports Nov 15, 2019
With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alteration...
Machine Learning Algorithms Databases, Factual Humans Neural Networks, Computer Genetic Variation Linear Models Reproducibility of Results Mutation Neoplasms Genetic Testing Genomics Workflow
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Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning.

Journal of medical microbiology Oct 30, 2019
Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted ...
Anti-Infective Agents Microbial Sensitivity Tests Supervised Machine Learning Time Factors Humans Acoustics Bacteria Software Flow Cytometry Workflow
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Technical Note: Ontology-guided radiomics analysis workflow (O-RAW).

Medical physics Oct 25, 2019
PURPOSE: Radiomics is the process to automate tumor feature extraction from medical images. This has shown potential for quantifying the tumor phenotype and predicting treatment response. The three major challenges of radiomics research and clinical ...
Biological Ontologies Image Processing, Computer-Assisted Workflow
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ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.

Scientific reports Oct 4, 2019
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely a...
Reproducibility of Results Pathology, Clinical Colorectal Neoplasms Colon Rectum Humans Deep Learning Workflow Image Processing, Computer-Assisted Uncertainty Bayes Theorem
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