首页|Findings from Washington University Provides New Data about Machine Learning (Pe rformance of Automated Classification of Diagnostic Entities In Dermatopathology Validated On Multisite Data Representing the Real-world Variability of Patholog y ...)
Findings from Washington University Provides New Data about Machine Learning (Pe rformance of Automated Classification of Diagnostic Entities In Dermatopathology Validated On Multisite Data Representing the Real-world Variability of Patholog y ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news originating from St. Louis, Missouri, by NewsRx c orrespondents, research stated, “Center dot Context.-More people receive a diagn osis of skin cancer each year in the United States than all other cancers combin ed. Many patients around the globe do not have access to highly trained dermatop athologists, whereas some biopsy diagnoses of patients who do have access result in disagreements between such specialists.” Our news journalists obtained a quote from the research from Washington Universi ty, “Mechanomind has developed software based on a deep-learning algorithm to cl assify 40 different diagnostic dermatopathology entities to improve diagnostic a ccuracy and to enable improvements in turnaround times and effort allocation. Ob jective.-To assess the value of machine learning for microscopic tissue evaluati on in dermatopathology.Design.-A retrospective study comparing diagnoses of hema toxylin and eosin-stained glass slides rendered by 2 senior board-certified path ologists not involved in algo- rithm creation with the machine learning algorith m’s classification was conducted. A total of 300 glass slides (1 slide per patie nt’s case) from 4 hospitals in the United States and Africa with common variatio ns in tissue preparation, staining, and scanning methods were included in the st udy.”
St. LouisMissouriUnited StatesNort h and Central AmericaAlgorithmsCyborgsDermatopathologyDiagnostics and Sc reeningEmerging TechnologiesHealth and MedicineMachine LearningPathologyWashington University
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