Few algorithms for histopathological images in computational pathology

dc.contributor.guide	Sethi, Amit
dc.coverage.spatial	Electronics and Electrical Engineering
dc.creator.researcher	Vahadane, Abhishek
dc.date.accessioned	2023-04-21T06:30:17Z
dc.date.available	2023-04-21T06:30:17Z
dc.date.awarded	2017
dc.date.completed	2017
dc.date.registered	2011
dc.description.abstract	In computational pathology huge stacks of histological data are made available through advances in the microscopic image acquisition Clinicians cannot learn easily from this big data due to obvious human limitations However machine learning systems can learn important patterns from these huge histological data to improve the diagnosis and prognosis Such automated prediction can act as a second opinion for the experts to help make their decisions In this thesis we address four different pr
dc.description.note	Not Available
dc.format.accompanyingmaterial	None
dc.format.dimensions	Not Available
dc.format.extent	Not Available
dc.identifier.uri	http://hdl.handle.net/10603/478387
dc.language	English
dc.publisher.institution	DEPARTMENT OF ELECTRONICS AND ELECTRICAL ENGINEERING
dc.publisher.place	Guwahati
dc.publisher.university	Indian Institute of Technology Guwahati
dc.relation	Not Available
dc.rights	self
dc.source.university	University
dc.subject.keyword	Engineering
dc.subject.keyword	Engineering and Technology
dc.subject.keyword	Engineering Electrical and Electronic
dc.title	Few algorithms for histopathological images in computational pathology
dc.title.alternative	Not available
dc.type.degree	Ph.D.