Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Hou, Benjamin"'
Autor:
Chen, Qingyu, Keenan, Tiarnan D L, Agron, Elvira, Allot, Alexis, Guan, Emily, Duong, Bryant, Elsawy, Amr, Hou, Benjamin, Xue, Cancan, Bhandari, Sanjeeb, Broadhead, Geoffrey, Cousineau-Krieger, Chantal, Davis, Ellen, Gensheimer, William G, Grasic, David, Gupta, Seema, Haddock, Luis, Konstantinou, Eleni, Lamba, Tania, Maiberger, Michele, Mantopoulos, Dimosthenis, Mehta, Mitul C, Nahri, Ayman G, AL-Nawaflh, Mutaz, Oshinsky, Arnold, Powell, Brittany E, Purt, Boonkit, Shin, Soo, Stiefel, Hillary, Thavikulwat, Alisa T, Wroblewski, Keith James, Chung, Tham Yih, Cheung, Chui Ming Gemmy, Cheng, Ching-Yu, Chew, Emily Y, Hribar, Michelle R., Chiang, Michael F., Lu, Zhiyong
Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and dive
Externí odkaz:
http://arxiv.org/abs/2409.15087
Autor:
Hou, Benjamin, Lee, Sung-Won, Lee, Jung-Min, Koh, Christopher, Xiao, Jing, Pickhardt, Perry J., Summers, Ronald M.
Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer. Materials and Methods: This retrospective study included contr
Externí odkaz:
http://arxiv.org/abs/2406.15979
Autor:
Hou, Benjamin, Zhu, Qingqing, Mathai, Tejas Sudarshan, Jin, Qiao, Lu, Zhiyong, Summers, Ronald M.
In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally Reconstructed Radiographs (DRRs) from 21,304 unique patients. Each im
Externí odkaz:
http://arxiv.org/abs/2406.03688
Autor:
Zhuang, Yan, Mathai, Tejas Sudharshan, Mukherjee, Pritam, Khoury, Brandon, Kim, Boah, Hou, Benjamin, Rabbee, Nusrat, Suri, Abhinav, Summers, Ronald M.
Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy. Current approaches have focused on delineating a limited set of abdominal structures (13 types).
Externí odkaz:
http://arxiv.org/abs/2405.05944
Autor:
Zhu, Qingqing, Hou, Benjamin, Mathai, Tejas S., Mukherjee, Pritam, Jin, Qiao, Chen, Xiuying, Wang, Zhizheng, Cheng, Ruida, Summers, Ronald M., Lu, Zhiyong
Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for evaluation. This study aims to bridge this gap by introducing a novel
Externí odkaz:
http://arxiv.org/abs/2403.05680
Autor:
Zhu, Qingqing, Chen, Xiuying, Jin, Qiao, Hou, Benjamin, Mathai, Tejas Sudharshan, Mukherjee, Pritam, Gao, Xin, Summers, Ronald M, Lu, Zhiyong
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical E
Externí odkaz:
http://arxiv.org/abs/2401.16578
Autor:
Hou, Benjamin, Mathai, Tejas Sudharshan, Liu, Jianfei, Parnell, Christopher, Summers, Ronald M.
Purpose: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical o
Externí odkaz:
http://arxiv.org/abs/2401.05294
Autor:
Zhuang, Yan, Hou, Benjamin, Mathai, Tejas Sudharshan, Mukherjee, Pritam, Kim, Boah, Summers, Ronald M.
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal
Externí odkaz:
http://arxiv.org/abs/2312.06453
Autor:
Zhao, Xuan, Hou, Benjamin
Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years. With the emergence of deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the nodule screening process, provi
Externí odkaz:
http://arxiv.org/abs/2305.01138
Autor:
Hou, Benjamin
Automatic segmentation of retina vessels plays a pivotal role in clinical diagnosis of prevalent eye diseases, such as, Diabetic Retinopathy or Age-related Macular Degeneration. Due to the complex construction of blood vessels, with drastically varyi
Externí odkaz:
http://arxiv.org/abs/2302.09215