CROWDSOURCING AND DEEP LEARNING BASED SEGMENTING AND KARYOTYPING OF CHROMOSOMES

CROWDSOURCING AND DEEP LEARNING BASED SEGMENTING AND KARYOTYPING OF CHROMOSOMES The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Ted...

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Bibliographische Detailangaben
Hauptverfasser:	VIG, Lovekesh, SRIRAMAN, Anand, KARANDE, Shirish Subhash, HEBBALAGUPPE, Ramya Sugnana Murthy, SHARMA, Monika
Format:	Patent
Sprache:	eng
Schlagworte:	BEER BIOCHEMISTRY CALCULATING CHEMISTRY COMPOSITIONS OR TEST PAPERS THEREFOR COMPUTING CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES COUNTING ENZYMOLOGY IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS METALLURGY MICROBIOLOGY MUTATION OR GENETIC ENGINEERING PHYSICS PROCESSES OF PREPARING SUCH COMPOSITIONS SPIRITS TESTING VINEGAR WINE
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Zusammenfassung:	CROWDSOURCING AND DEEP LEARNING BASED SEGMENTING AND KARYOTYPING OF CHROMOSOMES The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN). $ 4 4, #9 OP*