Ananya Dutta
University of Memphis
Machine learningOpticsPhysicsCancerEncoding (memory)Feature selectionArtificial intelligenceWavefrontOptical transfer functionSensitivity (control systems)Survival rateDiseaseDemographicsPancreatic cancerFluorescence-lifetime imaging microscopyPoint spread functionResolution (electron density)Spherical aberrationMedicineFeature (computer vision)Discriminative model
2Publications
1H-index
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Publications 2
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#1Ananya Dutta (U of M: University of Memphis)H-Index: 1
#2Bonny Banerjee (U of M: University of Memphis)H-Index: 11
Last. Subhash C. Chauhan (University of Texas at Austin)H-Index: 5
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Background: Pancreatic cancer (PC) is a disease with poor prognosis and survival rate. There is a pertinent need to identify the risk factors of this disease. The purpose of this study is to use machine learning methods to identify a subset of factors (a.k.a. features) from the PLCO dataset as predictors of PC. The Prostate, Lung, Colorectal and Ovarian (PLCO) cancer dataset is collected by the National Cancer Institute from 155,000 participants (49.5% male). Each participant responded to three ...
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#1Ana Doblas (U of M: University of Memphis)H-Index: 11
#2Ananya Dutta (U of M: University of Memphis)H-Index: 1
Last. Chrysanthe Preza (U of M: University of Memphis)H-Index: 13
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Previously, a wavefront encoded (WFE) imaging system implemented using a squared cubic (SQUBIC) phase mask has been verified to reduce the sensitivity of the imaging system to spherical aberration (SA). The strength of the SQUBIC phase mask and, as consequence, the performance of the WFE system are controlled by a design parameter, A. Although the higher the A-value, the more tolerant the WFE system is to SA, this is accomplished at the expense of the effective imaging resolution. In this contri...
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