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Description:: Enhance the segmentation accuracy of the Segment Anything Model on domain-specific images by applying active learning strategies with the aim to reduce human annotation expenses
Year:: 2023
Contributor:: Hu, Yifan (Creator); Yvernes, Clément (Creator)
Subject:: Active Learning; Image Segmentation; Mitochondrial; Segment Anything Model
Collection:: The Leadership Alliance Digital Library

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Description:: Renal cell carcinoma (RCC) is a life-threatening form of cancer causing tens of thousands of deaths in the USA yearly. Current treatment solutions mainly rely …
Year:: 2025
Contributor:: Franco, Alvaro (creator); Kimia, Benjamin B. (Advisor); Crisco, Joseph J. (Reader); Jiao, Zhicheng (Reader); Brown University. Biology and Medicine: Biomedical Engineering (sponsor)
Genre:: theses
Subject:: Biomedical engineering; Artificial intelligence; Oncology; Deep Learning; Image Segmentation; Diagnostic imaging; Kidneys--Tumors
Collection:: Biology and Medicine; Biomedical Engineering; Theses and Dissertations

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Description:: Modeling pairwise pixel interactions and how image segments can be distinguished from one another are common starting points of many segmentation approaches. In this thesis, …
Year:: 2021
Contributor:: Farias Sales Rocha Neto, Jeova (creator); Felzenszwalb, Pedro (Advisor); Harrison, Matthew (Reader); Kimia, Benjamin (Reader); Brown University. Engineering: Electrical Sciences and Computer Engineering (sponsor)
Genre:: theses
Subject:: Image analysis; Image Segmentation; Spectral theory (Mathematics); Mathematical models; Graph Cuts
Collection:: Engineering; Electrical Sciences and Computer Engineering; Theses and Dissertations

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Description:: Brain cancer remains a pervasive public health issue. The use of CNN’s to segment brain tumor images has gained significant traction since 2012; however, most …
Year:: 2020
Contributor:: Montagut, Julian (creator); Neretti, Nicola (Reader); Srivastava, Vikas (Reader); Lee, Jonghwan (Advisor); Brown University. Biology and Medicine: Biomedical Engineering (sponsor)
Genre:: theses
Subject:: Machine Learning; Histology, Pathological; Artificial intelligence; Glioblastoma; Deep Learning; Image Segmentation; Brain--Cancer
Collection:: Biology and Medicine; Biomedical Engineering; Theses and Dissertations

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