Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Sultan, Rafi Ibn"'
Autor:
Khanduri, Prashant, Li, Chengyin, Sultan, Rafi Ibn, Qiang, Yao, Kliewer, Joerg, Zhu, Dongxiao
Recently, compositional optimization (CO) has gained popularity because of its applications in distributionally robust optimization (DRO) and many other machine learning problems. Large-scale and distributed availability of data demands the developme
Externí odkaz:
http://arxiv.org/abs/2311.12652
Autor:
Sultan, Rafi Ibn, Li, Chengyin, Zhu, Hui, Khanduri, Prashant, Brocanelli, Marco, Zhu, Dongxiao
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation. However, it struggles with geographical images like aerial and satellite imagery, especially when segmenting mobility infrastructure includi
Externí odkaz:
http://arxiv.org/abs/2311.11319
Autor:
Li, Chengyin, Khanduri, Prashant, Qiang, Yao, Sultan, Rafi Ibn, Chetty, Indrin, Zhu, Dongxiao
Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. However, in clinical settings, creating effective prompts is notably ch
Externí odkaz:
http://arxiv.org/abs/2308.14936
Autor:
Li, Chengyin, Qiang, Yao, Sultan, Rafi Ibn, Bagher-Ebadian, Hassan, Khanduri, Prashant, Chetty, Indrin J., Zhu, Dongxiao
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-base
Externí odkaz:
http://arxiv.org/abs/2210.03189
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.