Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Santamaria-Pang A"'
Autor:
Codella, Noel C. F., Jin, Ying, Jain, Shrey, Gu, Yu, Lee, Ho Hin, Abacha, Asma Ben, Santamaria-Pang, Alberto, Guyman, Will, Sangani, Naiteek, Zhang, Sheng, Poon, Hoifung, Hyland, Stephanie, Bannur, Shruthi, Alvarez-Valle, Javier, Li, Xue, Garrett, John, McMillan, Alan, Rajguru, Gaurav, Maddi, Madhu, Vijayrania, Nilesh, Bhimai, Rehaan, Mecklenburg, Nick, Jain, Rupal, Holstein, Daniel, Gaur, Naveen, Aski, Vijay, Hwang, Jenq-Neng, Lin, Thomas, Tarapov, Ivan, Lungren, Matthew, Wei, Mu
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT,
Externí odkaz:
http://arxiv.org/abs/2410.06542
Brain networks display a hierarchical organization, a complexity that poses a challenge for existing deep learning models, often structured as flat classifiers, leading to difficulties in interpretability and the 'black box' issue. To bridge this gap
Externí odkaz:
http://arxiv.org/abs/2404.10031
Autor:
Abacha, Asma Ben, Santamaria-Pang, Alberto, Lee, Ho Hin, Merkow, Jameson, Cai, Qin, Devarakonda, Surya Teja, Islam, Abdullah, Gong, Julia, Lungren, Matthew P., Lin, Thomas, Codella, Noel C, Tarapov, Ivan
The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for enhancing healthcare outcomes if effectively leveraged. 3D image retriev
Externí odkaz:
http://arxiv.org/abs/2311.13752
Autor:
Lee, Ho Hin, Santamaria-Pang, Alberto, Merkow, Jameson, Oktay, Ozan, Pérez-García, Fernando, Alvarez-Valle, Javier, Tarapov, Ivan
We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions. RegionMIR addresses two major challenges for medical image
Externí odkaz:
http://arxiv.org/abs/2305.05598
Autor:
Latheef, Ammar Ahmed Pallikonda, Ghate, Sejal, Hui, Zhipeng, Santamaria-Pang, Alberto, Tarapov, Ivan, Sair, Haris I, Jones, Craig K
Resting State Networks (RSNs) of the brain extracted from Resting State functional Magnetic Resonance Imaging (RS-fMRI) are used in the pre-surgical planning to guide the neurosurgeon. This is difficult, though, as expert knowledge is required to lab
Externí odkaz:
http://arxiv.org/abs/2305.03814
Resting state fMRI is an imaging modality which reveals brain activity localization through signal changes, in what is known as Resting State Networks (RSNs). This technique is gaining popularity in neurosurgical pre-planning to visualize the functio
Externí odkaz:
http://arxiv.org/abs/2209.08200
Autor:
Santamaria-Pang, Alberto, Qiu, Jianwei, Chowdhury, Aritra, Kubricht, James, Tu, Peter, Naresh, Iyer, Virani, Nurali
We propose a novel framework for real-time black-box universal attacks which disrupts activations of early convolutional layers in deep learning models. Our hypothesis is that perturbations produced in the wavelet space disrupt early convolutional la
Externí odkaz:
http://arxiv.org/abs/2107.12473
The coronavirus disease (COVID-19) has resulted in a pandemic crippling the a breadth of services critical to daily life. Segmentation of lung infections in computerized tomography (CT) slices could be be used to improve diagnosis and understanding o
Externí odkaz:
http://arxiv.org/abs/2008.09866
Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they are severely hindered by their ina-bility to explain the reasoning behin
Externí odkaz:
http://arxiv.org/abs/2008.09860
Publikováno v:
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 1604-1607
We present ESCELL, a method for developing an emergent symbolic language of communication between multiple agents reasoning about cells. We show how agents are able to cooperate and communicate successfully in the form of symbols similar to human lan
Externí odkaz:
http://arxiv.org/abs/2007.09469