Zobrazeno 1 - 10
of 236
pro vyhledávání: '"Nadeem, Saad"'
Histology slide digitization is becoming essential for telepathology (remote consultation), knowledge sharing (education), and using the state-of-the-art artificial intelligence algorithms (augmented/automated end-to-end clinical workflows). However,
Externí odkaz:
http://arxiv.org/abs/2405.08169
While developing new unsupervised domain translation methods for endoscopy videos, it is typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, addit
Externí odkaz:
http://arxiv.org/abs/2310.00868
We propose an interactive visual analytics tool, Vis-SPLIT, for partitioning a population of individuals into groups with similar gene signatures. Vis-SPLIT allows users to interactively explore a dataset and exploit visual separations to build a cla
Externí odkaz:
http://arxiv.org/abs/2309.04423
Autor:
Ghahremani, Parmida, Marino, Joseph, Hernandez-Prera, Juan, de la Iglesia, Janis V., Slebos, Robbert JC, Chung, Christine H., Nadeem, Saad
We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multi
Externí odkaz:
http://arxiv.org/abs/2305.16465
This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground tru
Externí odkaz:
http://arxiv.org/abs/2301.11422
Autor:
Tiard, Alexandre, Wong, Alex, Ho, David Joon, Wu, Yangchao, Nof, Eliram, Goh, Alvin C., Soatto, Stefano, Nadeem, Saad
We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin (H&E) stained images of breast cancer. Our method is robust to stain variations inherent to the histology images acquisition process, which has limit
Externí odkaz:
http://arxiv.org/abs/2211.07590
Dose volume histogram (DVH) metrics are widely accepted evaluation criteria in the clinic. However, incorporating these metrics into deep learning dose prediction models is challenging due to their non-convexity and non-differentiability. We propose
Externí odkaz:
http://arxiv.org/abs/2207.03414
Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preproc
Externí odkaz:
http://arxiv.org/abs/2206.14951
Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria
Externí odkaz:
http://arxiv.org/abs/2206.14903
In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cel
Externí odkaz:
http://arxiv.org/abs/2204.04494