Zobrazeno 1 - 10
of 1 184
pro vyhledávání: '"Jeya, P."'
Autor:
Huo, Zepeng, Fries, Jason Alan, Lozano, Alejandro, Valanarasu, Jeya Maria Jose, Steinberg, Ethan, Blankemeier, Louis, Chaudhari, Akshay S., Langlotz, Curtis, Shah, Nigam H.
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for
Externí odkaz:
http://arxiv.org/abs/2411.09361
Autor:
Quan, Pengrui, Ouyang, Xiaomin, Jeyakumar, Jeya Vikranth, Wang, Ziqi, Xing, Yang, Srivastava, Mani
Effective processing, interpretation, and management of sensor data have emerged as a critical component of cyber-physical systems. Traditionally, processing sensor data requires profound theoretical knowledge and proficiency in signal-processing too
Externí odkaz:
http://arxiv.org/abs/2410.10741
Autor:
Ge, Aaron, Saha, Monjoy, Duggan, Maire A., Lenz, Petra, Abubakar, Mustapha, García-Closas, Montserrat, Balasubramanian, Jeya, Almeida, Jonas S., Bhawsar, Praphulla MS
Background: Tissue Microarrays (TMAs) significantly increase analytical efficiency in histopathology and large-scale epidemiologic studies by allowing multiple tissue cores to be scanned on a single slide. The individual cores can be digitally extrac
Externí odkaz:
http://arxiv.org/abs/2407.21233
Genotype imputation enhances genetic data by predicting missing SNPs using reference haplotype information. Traditional methods leverage linkage disequilibrium (LD) to infer untyped SNP genotypes, relying on the similarity of LD structures between ge
Externí odkaz:
http://arxiv.org/abs/2407.09355
Autor:
Blankemeier, Louis, Cohen, Joseph Paul, Kumar, Ashwin, Van Veen, Dave, Gardezi, Syed Jamal Safdar, Paschali, Magdalini, Chen, Zhihong, Delbrouck, Jean-Benoit, Reis, Eduardo, Truyts, Cesar, Bluethgen, Christian, Jensen, Malte Engmann Kjeldskov, Ostmeier, Sophie, Varma, Maya, Valanarasu, Jeya Maria Jose, Fang, Zhongnan, Huo, Zepeng, Nabulsi, Zaid, Ardila, Diego, Weng, Wei-Hung, Junior, Edson Amaro, Ahuja, Neera, Fries, Jason, Shah, Nigam H., Johnston, Andrew, Boutin, Robert D., Wentland, Andrew, Langlotz, Curtis P., Hom, Jason, Gatidis, Sergios, Chaudhari, Akshay S.
Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current radiologist shortage, there is a large impetus to use artificial intelligence to alleviate the
Externí odkaz:
http://arxiv.org/abs/2406.06512
Autor:
Deshpande, Tanvi, Prakash, Eva, Ross, Elsie Gyang, Langlotz, Curtis, Ng, Andrew, Valanarasu, Jeya Maria Jose
The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to train deep learning models for medical imaging tasks. In this work, we pr
Externí odkaz:
http://arxiv.org/abs/2404.17033
Autor:
Liu, Chih-Ying, Valanarasu, Jeya Maria Jose, Gonzalez, Camila, Langlotz, Curtis, Ng, Andrew, Gatidis, Sergios
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context of automated anatomy segmentation in whole-body Computed Tomography (CT
Externí odkaz:
http://arxiv.org/abs/2404.13185
Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backbone for diffusion models. Transformer-based models have shown better g
Externí odkaz:
http://arxiv.org/abs/2404.09976
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation. For most applications, we can train the model end-toend with paired data to obtai
Externí odkaz:
http://arxiv.org/abs/2404.09977
Autor:
Chen, Zhihong, Varma, Maya, Delbrouck, Jean-Benoit, Paschali, Magdalini, Blankemeier, Louis, Van Veen, Dave, Valanarasu, Jeya Maria Jose, Youssef, Alaa, Cohen, Joseph Paul, Reis, Eduardo Pontes, Tsai, Emily B., Johnston, Andrew, Olsen, Cameron, Abraham, Tanishq Mathew, Gatidis, Sergios, Chaudhari, Akshay S., Langlotz, Curtis
Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice. Recent advances in the development of vision-language foundation models (FMs) give rise to the possibility of performing automated CXR interpretation, which can
Externí odkaz:
http://arxiv.org/abs/2401.12208