Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Hoogi, Assaf"'
X-ray imaging is a fundamental clinical tool for screening and diagnosing various diseases. However, the spatial resolution of radiographs is often limited, making it challenging to diagnose small image details and leading to difficulties in identify
Externí odkaz:
http://arxiv.org/abs/2306.03983
The performance improvement of deep networks significantly depends on their optimizers. With existing optimizers, precise and efficient recognition of the gradients trend remains a challenge. Existing optimizers predominantly adopt techniques based o
Externí odkaz:
http://arxiv.org/abs/2306.01423
Autor:
Hatamizadeh, Ali, Hoogi, Assaf, Sengupta, Debleena, Lu, Wuyue, Wilcox, Brian, Rubin, Daniel, Terzopoulos, Demetri
Publikováno v:
MLMI 2019
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS),
Externí odkaz:
http://arxiv.org/abs/1908.06933
Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibit
Externí odkaz:
http://arxiv.org/abs/1907.02431
We propose a novel architecture for object classification, called Self-Attention Capsule Networks (SACN). SACN is the first model that incorporates the Self-Attention mechanism as an integral layer within the Capsule Network (CapsNet). While the Self
Externí odkaz:
http://arxiv.org/abs/1904.12483
Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning. At the same time, va
Externí odkaz:
http://arxiv.org/abs/1802.04403
Autor:
Gaziv, Guy, Beliy, Roman, Granot, Niv, Hoogi, Assaf, Strappini, Francesca, Golan, Tal, Irani, Michal
Publikováno v:
In NeuroImage 1 July 2022 254
We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes. The process is performed in three steps, including organ detection, lesion detection and le
Externí odkaz:
http://arxiv.org/abs/1703.06418
Autor:
Hoogi, Assaf, Beaulieu, Christopher F., Cunha, Guilherme M., Heba, Elhamy, Sirlin, Claude B., Napel, Sandy, Rubin, Daniel L.
We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method c
Externí odkaz:
http://arxiv.org/abs/1606.03765
Autor:
Reposar, Aaron L., Mabud, Tarub S., Eifler, Aaron C., Hoogi, Assaf, Arendt, Victoria, Cohn, David M., Rubin, Daniel L., Hofmann, Lawrence V.
Publikováno v:
In Journal of Vascular and Interventional Radiology February 2020 31(2):270-275